MALNUTRITION AND CLINICAL OUTCOMES IN ELDERLY PATIENTS ... · Malnutrition and Clinical Outcomes in Elderly Patients from A Singapore Acute Hospital ii Abstract Older adults, especially

MALNUTRITION AND CLINICAL OUTCOMES

IN ELDERLY PATIENTS FROM A

SINGAPORE ACUTE HOSPITAL

Thesis submitted by

YEN PENG LIM

Bachelor of Science (Nutrition and Dietetics) (Hons)

Master of Health Science (Gerontology)

A thesis submitted for the degree of Doctor of Philosophy

in the

Institute of Health and Biomedical Innovation

School of Public Health

Queensland University of Technology

2010

Malnutrition and Clinical Outcomes in Elderly Patients from A Singapore Acute Hospital

i

Keywords

Acute care; body mass index (BMI); clinical outcomes; corrected arm muscle area

(CAMA); covariates; dementia; depression; dietitian referral; discharge destination;

elderly; functional status; geriatric; hospitalised; length of stay (LOS); malnutrition;

mid-arm circumference (MAC); Mini Nutritional Assessment (MNA); Mini

Nutritional Assessment Short Form (MNA-SF); Modified Barthel Index (MBI);

modified texture diet; mortality; nutrition assessment; nutrition risk screening (NRS);

nutrition screening; older adults; prevalence; readmission; Singapore; Short

Nutritional Assessment Questionnaire (SNAQ); Subjective Global Assessment

(SGA); swallowing impairment; triceps skinfold thickness (TSF); undiagnosed

malnutrition; validity; weight loss; >60 years


ii

Abstract

Older adults, especially those acutely ill, are vulnerable to developing malnutrition

due to a range of risk factors. The high prevalence and extensive consequences of

malnutrition in hospitalised older adults have been reported extensively. However,

there are few well-designed longitudinal studies that report the independent

relationship between malnutrition and clinical outcomes after adjustment for a wide

range of covariates. Acutely ill older adults are exceptionally prone to nutritional

decline during hospitalisation, but few reports have studied this change and impact on

clinical outcomes. In the rapidly ageing Singapore population, all this evidence is

lacking, and the characteristics associated with the risk of malnutrition are also not

well-documented.

Despite the evidence on malnutrition prevalence, it is often under-recognised and

under-treated. It is therefore crucial that validated nutrition screening and assessment

tools are used for early identification of malnutrition. Although many nutrition

screening and assessment tools are available, there is no universally accepted method

for defining malnutrition risk and nutritional status. Most existing tools have been

validated amongst Caucasians using various approaches, but they are rarely reported

in the Asian elderly and none has been validated in Singapore. Due to the multi-

ethnicity, cultural, and language differences in Singapore older adults, the results from

non-Asian validation studies may not be applicable. Therefore it is important to

identify validated population and setting specific nutrition screening and assessment

methods to accurately detect and diagnose malnutrition in Singapore.


iii

The aims of this study are therefore to: i) characterise hospitalised elderly in a

Singapore acute hospital; ii) describe the extent and impact of admission

malnutrition; iii) identify and evaluate suitable methods for nutritional screening and

assessment; and iv) examine changes in nutritional status during admission and their

impact on clinical outcomes.

A total of 281 participants, with a mean (+SD) age of 81.3 (+7.6) years, were

recruited from three geriatric wards in Tan Tock Seng Hospital over a period of eight

months. They were predominantly Chinese (83%) and community-dwellers (97%).

They were screened within 72 hours of admission by a single dietetic technician using

four nutrition screening tools [Tan Tock Seng Hospital Nutrition Screening Tool

(TTSH NST), Nutritional Risk Screening 2002 (NRS 2002), Mini Nutritional

Assessment-Short Form (MNA-SF), and Short Nutritional Assessment Questionnaire

(SNAQ©)] that were administered in no particular order. The total scores were not

computed during the screening process so that the dietetic technician was blinded to

the results of all the tools. Nutritional status was assessed by a single dietitian, who

was blinded to the screening results, using four malnutrition assessment methods

[Subjective Global Assessment (SGA), Mini Nutritional Assessment (MNA), body

mass index (BMI), and corrected arm muscle area (CAMA)]. The SGA rating was

completed prior to computation of the total MNA score to minimise bias. Participants

were reassessed for weight, arm anthropometry (mid-arm circumference, triceps

skinfold thickness), and SGA rating at discharge from the ward. The nutritional

assessment tools and indices were validated against clinical outcomes (length of stay

(LOS) >11days, discharge to higher level care, 3-month readmission, 6-month

mortality, and 6-month Modified Barthel Index) using multivariate logistic regression.


iv

The covariates included age, gender, race, dementia (defined using DSM IV criteria),

depression (defined using a single question “Do you often feel sad or depressed?”),

severity of illness (defined using a modified version of the Severity of Illness Index),

comorbidities (defined using Charlson Comorbidity Index, number of prescribed

drugs and admission functional status (measured using Modified Barthel Index; MBI).

The nutrition screening tools were validated against the SGA, which was found to be

the most appropriate nutritional assessment tool from this study (refer section 5.6)

Prevalence of malnutrition on admission was 35% (defined by SGA), and it was

significantly associated with characteristics such as swallowing impairment

(malnourished vs well-nourished: 20% vs 5%), poor appetite (77% vs 24%), dementia

(44% vs 28%), depression (34% vs 22%), and poor functional status (MBI 48.3+29.8

vs 65.1+25.4). The SGA had the highest completion rate (100%) and was predictive

of the highest number of clinical outcomes: LOS >11days (OR 2.11, 95% CI [1.17-

3.83]), 3-month readmission (OR 1.90, 95% CI [1.05-3.42]) and 6-month mortality

(OR 3.04, 95% CI [1.28-7.18]), independent of a comprehensive range of covariates

including functional status, disease severity and cognitive function. SGA is therefore

the most appropriate nutritional assessment tool for defining malnutrition. The TTSH

NST was identified as the most suitable nutritional screening tool with the best

diagnostic performance against the SGA (AUC 0.865, sensitivity 84%, specificity

79%). Overall, 44% of participants experienced weight loss during hospitalisation,

and 27% had weight loss >1% per week over median LOS 9 days (range 2-50). Well-

nourished (45%) and malnourished (43%) participants were equally prone to

experiencing decline in nutritional status (defined by weight loss >1% per week).


v

Those with reduced nutritional status were more likely to be discharged to higher

level care (adjusted OR 2.46, 95% CI [1.27-4.70]).

This study is the first to characterise malnourished hospitalised older adults in

Singapore. It is also one of the very few studies to (a) evaluate the association of

admission malnutrition with clinical outcomes in a multivariate model; (b) determine

the change in their nutritional status during admission; and (c) evaluate the validity of

nutritional screening and assessment tools amongst hospitalised older adults in an

Asian population. Results clearly highlight that admission malnutrition and

deterioration in nutritional status are prevalent and are associated with adverse clinical

outcomes in hospitalised older adults. With older adults being vulnerable to risks and

consequences of malnutrition, it is important that they are systematically screened so

timely and appropriate intervention can be provided. The findings highlighted in this

thesis provide an evidence base for, and confirm the validity of the current nutrition

screening and assessment tools used among hospitalised older adults in Singapore. As

the older adults may have developed malnutrition prior to hospital admission, or

experienced clinically significant weight loss of >1% per week of hospitalisation,

screening of the elderly should be initiated in the community and continuous

nutritional monitoring should extend beyond hospitalisation.


vi


vii

Table of Contents

Keywords.........................................................................................................................i Abstract...........................................................................................................................ii Table of Contents ..........................................................................................................vii List of Tables .................................................................................................................ix List of Figures ...............................................................................................................xii List of Abbreviations....................................................................................................xiii Publications by the candidate ........................................................................................xv Statement of Original Authorship ................................................................................xvii Acknowledgements ....................................................................................................xviii Background.....................................................................................................................1 1 Literature Review ................................................................................................5

1.1 Introduction .........................................................................................................5 1.2 Definitions of key concepts..................................................................................6 1.3 Literature search strategy ................................................................................... 11 1.4 Risk factors for malnutrition .............................................................................. 12 1.5 Prevalence of malnutrition ................................................................................. 16 1.6 Consequences of malnutrition ............................................................................ 32 1.7 Nutrition screening ............................................................................................ 43 1.8 Nutrition assessment .......................................................................................... 59 1.9 Summary of research gaps ................................................................................. 84 1.10 Research aims and questions.............................................................................. 88

2 Methods.............................................................................................................90 2.1 Sample type and size.......................................................................................... 90 2.2 Measurements.................................................................................................... 91 2.3 Statistical Analysis........................................................................................... 114

3 Recruitment and participants’ characteristics by age ........................................122 3.1 Introduction and aims ...................................................................................... 122 3.2 Recruitment ..................................................................................................... 122 3.3 Participants’ (n= 281) characteristics ............................................................... 127 3.4 Discussion ....................................................................................................... 137 3.5 Conclusion....................................................................................................... 139

4 Prevalence of malnutrition and comparison of participants’ characteristics by nutritional status..........................................................................................................140

4.1 Introduction and aims ...................................................................................... 140 4.2 Methods........................................................................................................... 140 4.3 Results............................................................................................................. 142 4.4 Discussion ....................................................................................................... 155 4.5 Conclusion....................................................................................................... 162

5 Nutritional status and clinical outcomes...........................................................164 5.1 Introduction and aims ...................................................................................... 164 5.2 Methods........................................................................................................... 165 5.3 Results............................................................................................................. 166 5.4 Discussion ....................................................................................................... 177 5.5 Conclusion....................................................................................................... 192

6 Criterion-related validity of nutrition screening tools .......................................194 6.1 Introduction and aims ...................................................................................... 194


viii

6.2 Methods........................................................................................................... 195 6.3 Results............................................................................................................. 196 6.4 Discussion ....................................................................................................... 207 6.5 Conclusion....................................................................................................... 214

7 Changes in anthropometric measurements and nutritional status during hospitalisation, and impact on clinical outcomes..........................................................216

7.1 Introduction and aims ...................................................................................... 216 7.2 Methods........................................................................................................... 217 7.3 Results............................................................................................................. 220 7.4 Discussion ....................................................................................................... 239 7.5 Conclusion....................................................................................................... 250

8 Study significance, strengths, limitations and recommendations.......................252 8.1 Overview of study significance and outcomes.................................................. 252 8.2 Summary of key findings................................................................................. 254 8.3 Strengths and contributions to new knowledge................................................. 258 8.4 Limitations ...................................................................................................... 267 8.5 Recommendations............................................................................................ 274 8.6 Conclusion....................................................................................................... 285

9 Appendices......................................................................................................286 10 References .......................................................................................................337


ix

List of Tables Table 1-1: Definitions of malnutrition from key organisations································· 7 Table 1-2: Risk factors for malnutrition among older adults····································13 Table 1-3: Malnutrition risks associated with hospitalisation···································14 Table 1-4: List of common malnutrition assessment methods used in studies and their

associated range of malnutrition prevalence····················································18 Table 1-5: Comparison of malnutrition (defined by SGA) prevalence between

younger and older hospitalised adults ·····························································19 Table 1-6: Prevalence of malnutrition in hospitalised older adults (>60 years) from

acute care ·····································································································23 Table 1-7: Prevalence of malnutrition in older adults (>60 years) in sub-acute,

community and long term care settings···························································24 Table 1-8: Studies which examined changes in nutritional status during hospitalisation

····················································································································31 Table 1-9: Common adverse clinical consequences of malnutrition ·························32 Table 1-10: Longitudinal studies of the impact of malnutrition on clinical outcomes in

hospitalised elderly without adjustment for covariates·····································34 Table 1-11: Longitudinal studies of the impact of malnutrition on clinical outcomes in

hospitalised elderly, adjusting for covariates···················································36 Table 1-12: Benefits of nutrition screening ····························································44 Table 1-13: Common limitations of available nutrition screening tools····················47 Table 1-14: Characteristics of an effective nutrition screening tool··························48 Table 1-15: Comparison of characteristics of selected nutrition screening tools········49 Table 1-16: List of parameters and indices used in nutrition assessment ··················60 Table 1-17: Predictive equations for height using knee height ·································65 Table 1-18: Equations for derivative arm indicators················································70 Table 1-19: Comparison of characteristics between Subjective Global Assessment

(SGA) and MiniNutritional Assessment (MNA) ·············································80 Table 1-20: Comparisons on the applications of commonly used nutrition parameters

in clinical practice·························································································83 Table 2-1: Variables and measures collected at admission/baseline – Phase 1 ··········96 Table 2-2: Classification of BMI based on WHO international classification ···········99 Table 2-3: Derived arm anthropometric measures and associated equations ···········101 Table 2-4: Modified Barthel Index·······································································106 Table 2-5: Classification of dependency levels used by the Modified Barthel Index106 Table 2-6: Charlson Comorbidity Index·······························································107 Table 2-7: Modified Severity of Illness Index (SII)···············································108 Table 2-8: Variables and measures collected upon discharge and post discharge –

Phase 2·······································································································112 Table 2-9: List and types of variables for application in statistical analysis ············120 Table 3-1: Reasons for non-eligibility across the three study wards (n=258) ··········125 Table 3-2: Comparison between all GRM patients, non-eligible patients from the three

study wards and study participants during the study period (November 2006 to July 2007)···································································································125

Table 3-3: Comparison between non-respondents and study participants ···············126 Table 3-4: Sociodemographic characteristics of 281 hospitalised older adults (>60

years) and according to age groups·······························································130


x

Table 3-5: Nutritional characteristics of 281 hospitalised older adults (>60 years) and according to age groups···············································································131

Table 3-6: Appetite, chewing and swallowing characteristics of 281 hospitalised older adults (>60 years) and according to age groups·············································133

Table 3-7: Clinical characteristics of 281 hospitalised older adults (>60 years) and according to age groups···············································································134

Table 3-8: Clinical outcomes of 281 hospitalised older adults (>60 years) and according to age groups···············································································136

Table 4-1 : Comparison of participants’ characteristics and malnutrition prevalence between those with and without weight measurements on admission··············144

Table 4-2: Classification and agreement of the four different malnutrition assessment methods applied to hospitalised older adults (>60 years) ·······························145

Table 4-3: Sociodemographic characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission·········································148

Table 4-4: Appetite, chewing and swallowing characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission ················150

Table 4-5: Clinical characteristics of 281 older adults aged >60 years according to nutritional status on admission·····································································152

Table 4-6: Summary of patient characteristics significantly associated with malnutrition on admission as assessed by four different malnutrition assessment methods······································································································154

Table 5-1: Clinical outcomes of study participants (n=281) aged >60 years according to nutritional status as assessed by the four different malnutrition assessment methods······································································································169

Table 5-2: Summary of univariate relationships between clinical outcomes and nutritional status as assessed by the four different malnutrition assessment methods (expressed in odds ratio, 95% confidence interval). ·························170

Table 5-3: Summary of multivariate relationships between clinical outcomes and nutritional status as assessed by the four different malnutrition assessment methods (expressed in odds ratio, 95% confidence interval) ··························172

Table 5-4: Comparison of clinical outcomes between participants with and without admission weight measurements ··································································174

Table 5-5: Summary of the AUC from ROC curve analysis for each malnutrition assessment method against the clinical outcomes··········································176

Table 6-1: Classification of malnutrition risk in older adults >60 years by the various nutrition screening tools completed by dietetic technician ·····························197

Table 6-2: Validation of nutrition screening tools against SGA determined nutritional status··········································································································200

Table 6-3: TTSH NST-defined malnutrition risk as assessed by the dietetic technician and clinical outcomes··················································································206

Table 7-1: Anthropometric measurements and nutritional status of older adults (>60 years) on admission to and upon discharge from an acute care hospital [median LOS 9 days (range 2-50 days)]·····································································221

Table 7-2: Frequency and magnitude of decline in anthropometric measurements and nutritional status of older adults (>60 years) with admission and discharge assessment [median LOS 9 days (range 2-50 days)] ······································222

Table 7-3: Association of appetite, chewing, swallowing, clinical and nutritional characteristics of older adults (>60 years) with weight loss >1% per week during acute hospitalisation [median LOS 9 days (range 2-50 days)] ························226


xi

Table 7-4: Weight loss of >1% per week of hospitalisation among older adults (>60 years) and the associated clinical outcomes ··················································228

Table 7-5: Decline in CAMA during hospitalisation among older adults (>60 years) and the associated clinical outcomes ····························································229

Table 7-6: Comparison of participants’ characteristics and malnutrition prevalence between those with and without weight measurements at discharge ···············231

Table 7-7: Comparison of clinical outcomes between participants with and without discharge weight measurements ···································································232

Table 7-8: Comparison of participants’ characteristics and malnutrition prevalence between those with and without weight change data······································235

Table 7-9: Comparison of clinical outcomes between participants with and without weight change data······················································································236


xii

List of Figures Figure 1-1: Relationships between the key concepts in the research study ............... 10 Figure 4-1: Prevalence of malnutrition among older adults (>60 years) on admission

to specialist geriatric wards using different malnutrition assessment methods .143 Figure 6-1: ROC curves of nutrition screening tools against SGA (n=235) .............201 Figure 6-2: ROC curve of MNA-SF against SGA (n=235)......................................202 Figure 6-3: ROC curve of TTSH NST (completed by the dietetic technician) against

SGA for all participants aged >60 years (n=281) ............................................203 Figure 6-4: ROC curve of TTSH NST (completed by the dietetic technician) against

SGA for participants aged >85 years (n=95) ...................................................204 Figure 7-1: Flowchart of participants with and without weight measurement on

admission and discharge .................................................................................234 Figure 7-2: Flowchart of malnourished participants (defined by SGA) on admission

and the corresponding malnutrition risk routinely screened by nursing staff and the subsequent referral to dietitian ..................................................................238


xiii

List of Abbreviations ALB

ALOS

AMT

ASPEN

AUC

BAPEN

BMI

CAMA

CC

CCI

CI

DAA

ESPEN

GRM

IBW

LOS

MAC

MAMA

MAMC

MBI

MNA

MNA-SF

MST

Serum albumin

Average length of stay

Abbreviated Mental Test

American Society for Parenteral and Enteral Nutrition

Area under the curve

British Association for Parenteral and Enteral Nutrition

Body mass index

Corrected arm muscle area

Calf circumference

Charlson Comorbidity Index

Confidence interval

Dietitians Association of Australia

European Society for Parenteral and Enteral Nutrition

Geriatric Medicine

Ideal body weight

Length of stay

Mid-arm circumference

Mid-arm muscle area

Mid-arm muscle circumference

Modified Barthel Index

Mini Nutritional Assessment

Mini Nutritional Assessment- Short Form

Malnutrition Screening Tool


xiv

MUST

NRI

NRS 2002

NST

ROC

SGA

SNAQ©

SII

TLC

TSF

TTSH

WHO

Malnutrition Universal Screening Tool

Nutrition Risk Index

Nutritional Risk Screening 2002

Nutrition screening tool

Receiver operating characteristic

Subjective Global Assessment

Short Nutritional Assessment Questionnaire

Severity of Illness Index

Total lymphocyte count

Triceps skinfold thickness

Tan Tock Seng Hospital

World Health Organization


xv

Publications by the candidate

Conference presentations

Refereed conference presentations from results relating to thesis

Lim YP, Lim WS, Tan TL, Daniels L (2009) Change in nutritional status of older

adults during hospitalisation (Abstract), Annals of Nutrition and Metabolism,

55:(suppl 1), 308.

• Presented at International Congress of Nutrition (ICN) 2009, 4-9 Oct, Bangkok,

Thailand

• Presented at NHG Annual Scientific Congress 2009, 16-17 Oct, Singapore

Lim YP, Lim WS, Tan TL, Daniels L (2009) Prevalence, risk factors and outcomes

of malnutrition in hospitalized older adults (Abstract), Annals of Nutrition and

Metabolism, 55:(suppl 1), 308.

• Presented at NHG Annual Scientific Congress 2008, 7-8 Nov, Singapore

(Merit Award for Best Poster Presentation – Allied Health Category)

• Presented at International Congress of Nutrition (ICN) 2009, 4-9 Oct, Bangkok,

Thailand

Lim YP, Lim WS, Tan TL, Daniels L (2009) Subjective Global Assessment is

Clinically More Useful than Mini-Nutritional Assessment in Hospitalised Older

Adults (Abstract), Clinical Nutrition Supplements, 4:2,106.

• Presented at 31st ESPEN Congress 2009, 29 Aug-1Sep, Vienna, Austria

(Travel Award for Best first presented abstract)

• Presented at NHG Annual Scientific Congress 2009, 16-17 Oct, Singapore

Lim YP, Lim WS, Tan TL, Daniels L (2009) Evaluating the validity of four

nutritional screening tools in hospitalized older adults (Abstract), Clinical Nutrition

Supplements, 4:2,106.

• Presented at 31st ESPEN Congress 2009, 29 Aug-1Sep, Vienna, Austria


xvi

Lim YP, Lim WS, Tan TL, Daniels L. (2008) Evaluating the validity of a nutritional

screening tool in hospitalized older adults (Abstract), Annals of the Academy of

Medicine Singapore, S5.

• Presented at NHG Annual Scientific Congress 2008, 7-8 Nov, Singapore

(Winner for Best Oral Presentation – Allied Health Category)

Lim YP, Lim WS, Tan TL, Chan YH, Daniels L (2007) Nutritional Status of

Geriatric Patients in A Singapore Acute Hospital (Abstract), Journal of Nutrition,

Health & Aging,11:5, 406

• Presented at Medicine, Ageing and Nutrition 2007, 5-8 September, Adelaide,

Australia


xvii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a degree or

diploma at any other higher education institution. To the best of my knowledge and

belief, the thesis contains no material previously published or written by another

person except where due reference is made.

Signed: ____________ _____ Date: _____20 December 2010__________


xviii

Acknowledgements

I would like to extend my deepest appreciation to the following people who have

provided their assistance and support during the past four years of my PhD journey:

Professor Lynne Daniels, my principal supervisor, who agreed without any hesitation

to guide me along this long journey. She made it possible for me to embark on and

complete my PhD journey as an external candidate against all odds. I am grateful for

her time and patience in providing me with the most valuable comments and advice

on my PhD research and thesis.

Professor Susan Ash, my associate supervisor, who has been very reassuring and

constructive in her feedback for my thesis.

Dr Lim Wee Shiong and Dr Tan Thai Lian, Consultant Geriatricians who provided the

opportunity for collaboration with the Geriatric Medicine Department in Tan Tock

Seng Hospital to develop the research proposal. I appreciate them assuming the roles

of my external associate supervisors despite their heavy work commitments and

guiding me through the data collection phase.

Dr Chan Yiong Huak, biostatistician from Yong Loo Lin School of Medicine, who

readily accepted my request for his statistical support. I am thankful for his patience

in explaining all the statistical approaches and tests required for my thesis.

Dr Selvaganapathi Natesan, Registrar from Geriatric Medicine Department, who

completed the clinical assessments, such as the Severity of Illness Score and Charlson

Comorbidity Index for all participants.

Ms Sabrina Ow Yong, Ms Dinnie Ng, and Ms Tay Yi Chin, Occupational Therapists

who completed the pre-morbid and admission functional status assessments for all

participants.


xix

Ms Fun Zhengqiu, dietetic technician, who completed the nutritional screening for all

participants.

Ms Ng Kok Mun, manager of Nutrition and Dietetics Department, who supported my

study sponsorship application. She has been extremely understanding and generous

during this period of study while I had to reduce my work commitment.

All the administrative and nursing staff in Wards 7B, 7C, and 7D for their full

cooperation in ensuring smooth conduct of the study, and in providing assistance as

witnesses and translators during consent taking.

All the doctors from the Geriatric Medicine Department who never failed to show

their encouragement and support throughout the conduct of the research study, and for

being so generous in allowing the recruitment of their patients.

All the precious participants, their families and caregivers, who consented to the

study, and gave their precious time to complete all the assessments. Without them,

this study would not be possible.

Lastly and most importantly, my husband, Jamie, who was there for me all the time,

for being my source of inspiration and providing me with all the support I needed to

complete this long journey.


xx


1

Background

This section provides a background and an introduction to the study population and

setting. This information is essential for a better understanding of the importance and

relevance of the identified area of research discussed in this thesis.

Elderly population in Singapore

Singapore had a population of 5.0 million in 20091. The elderly population, like in

other developed countries, is increasing in Singapore. It has one of the fastest ageing

populations in Asia. Currently, about 9% of the population is aged 65 years and

above1. The numbers are predicted to rise rapidly over the next 20 years. In 2030,

19% of the population would be above 65 years2. In absolute terms, it would increase

from 296 900 in 2005 to 873 300 in 20302.

In the 2000 population census, there were 10% more female than male elderly

amongst those aged above 65 years. The ethnic composition of the elderly population

consisted of Chinese (80%), Malays (11%), Indians (8%), and others (1%)3. The main

religions were Buddhism (47%), Christianity (14%), Islam (13%), Taoism (13%) and

Hinduism (4%). Seventy per cent had no formal qualifications at the primary level3.

Literacy level was 60%, with 36%, 8%, 5%, 2% being literate in only Chinese,

English, Malay or Tamil respectively3. Chinese dialects were the most commonly

spoken languages (62%), followed by Malay (11%), English (7%), Mandarin (7%),

and Indian languages (4%). About 87% of the elderly were ambulant and physically

independent2. Older Singaporeans also make up a substantial proportion (36% in

2007) of all hospital admissions4. In Singapore, family remains as the first line of care


2

and support as 80% of the non-ambulant (bedbound and chairbound) elderly lived

with their children3.

Geriatric medicine in acute hospital

The first Department of Geriatric Medicine in Singapore was established at Tan Tock

Seng Hospital (TTSH) in 1988 in response to challenges of an ageing population on

health care services. The Department is an Internal Medicine specialty where patients

are admitted to the Geriatric Medicine unit in the hospital based on age related

conditions instead of using an age-defined cut-off. These conditions include geriatric

syndromes like gait instability, immobility, incontinence and intellectual impairment.

A multidisciplinary approach is adopted for a comprehensive assessment and

management of the geriatric patients in the unit.

Tan Tock Seng Hospital (TTSH) is a 1200-bed acute care hospital, and one of three

tertiary hospitals in Singapore. It has three geriatric specialist wards. Based on

Hospital statistics (TTSH Intranet, 2006), there were about 52 000 admissions in 2006

of which about 53% (27, 551) were patients aged > 60 years. The ethnic distribution

of the older hospitalised patients was Chinese (82%), Malay (8%), 7% Indian (7%),

and other (3%). There were about 1930 admissions to the 80-bed Geriatric Medicine

wards in a year, of which, 81% were admitted from the emergency department and

18% from specialist outpatient clinics. The overall average LOS for all patients in

TTSH was 7.1 days and average length of stay of patients from the Geriatric Medicine

was 10.9 days.


3

Pneumonia and urinary tract infection are the top two diagnostic reasons for

admission to TTSH. Since TTSH is an acute hospital, there are several discharge

destinations for patients when their acute medical problems are resolved. Depending

on the patients’ independence in function and their availability of care, they are either

discharged to their own home with or without a caregiver, or to an intermediate step-

down care facility such as a community rehabilitation hospital, or to a long-term care

institution such as a nursing home. Due to the acute shortages of beds in TTSH,

patients are discharged to either sub-acute care wards or community hospitals as soon

as their acute medical problems are resolved.

Identified area of research

Demands on healthcare services and resources for the elderly are expected to increase

with the ageing trends in Singapore. With the elderly population constituting a

significant proportion of admissions to TTSH, and with Geriatric Medicine being one

of the hospital’s key clinical strengths, this specific population warrants study. A

better understanding of the characteristics, nutrition and health risks of the elderly

would inform the stakeholders for appropriate planning and development of nutrition-

related services and programmes. The unique socio-demographic characteristics of

older Singaporeans and the Singapore healthcare system mean application of evidence

from other countries may not be entirely relevant or valid.

Hospitalised older adults are vulnerable to malnutrition and associated adverse

clinical outcomes due to physiological and socio-psychological factors associated

with ageing5-7. Hence malnutrition is clearly a pertinent research topic for

investigation in the Singaporean hospitalised elderly. It is therefore important for


4

more clinical nutrition research to be carried out in this area to contribute towards

improving the care and clinical outcomes of these elderly patients.

I have been working at TTSH for the past ten years and was involved in the

implementation of the hospital nutrition screening policy. My clinical experience in

the acute hospital and in particular with the Geriatric Medicine department has

exposed me to the various opportunities and challenges in providing medical nutrition

therapy and conducting dietetic research. With my particular interest in the nutrition

care of the elderly, I am strongly urged to focus on addressing the challenges of

malnutrition detection and diagnosis. From my clinical experience, I can relate with

the common problem of elderly malnutrition, however it has not been well-studied in

Singapore. In the search to identify the appropriate screening and assessment tools

that can be applied to the local population, I discovered the potential research gaps.

These will be highlighted in the following literature review (Chapter 1) and they will

be addressed in the thesis.


5

1 Literature Review

1.1 Introduction

The focus of this thesis is malnutrition in hospitalised older adults, its prevalence,

consequences, and methods of identification. The key concepts described in this thesis

are defined in section 1.2. The framework underpinning the research concepts and the

relationships between them are presented as a concept map in Figure 1-1.

The literature search strategies used in this review are explained in section 1.3. The

literature review includes a comprehensive analysis of identified published research,

defining the extent and impact of the malnutrition problem, its risk (section 1.4),

prevalence (section 1.5) and consequences (section 1.6). This is followed by a review

of methods to identify malnutrition including nutrition screening (section 1.7) and

assessment (section 1.8). This review highlights key issues and research gaps in this

topic, and forms the basis for the research questions in this thesis.


6

1.2 Definitions of key concepts

Malnutrition/Undernutrition

There is no single generally agreed definition of malnutrition and hence a “gold

standard” for assessment of malnutrition does not exist. Malnutrition definitions differ

between disciplines, cultures and institutions8. Malnutrition can be acute or chronic,

and may result from primary or secondary causes. Some definitions focus on the

imbalance of nutrition, while others encompass the clinical influence of this

imbalance (Table 1-1). Overall these concepts define malnutrition as a state of

suboptimal nutritional status associated with poor clinical outcomes.

Malnutrition is commonly used to refer to undernutrition in the literature, and the

terms tend to be used interchangeably. In the following literature review and context

of this study, malnutrition will be used to refer to protein-energy malnutrition

specifically and will exclude overnutrition. It does not specifically identify

sarcopenia9 or geriatric cachexia10, which also display similar signs and symptoms to

protein-energy malnutrition.


7

Table 1-1: Definitions of malnutrition from key organisations

ICD-10-AM Sixth Edition 11

E43 Unspecified severe protein-energy malnutrition

In adults, BMI < 18.5 kg/m2 or unintentional weight loss (≥ 10%) with evidence of

suboptimal intake resulting in severe loss of subcutaneous fat and/or severe muscle wasting.

E44 Protein-energy malnutrition of moderate and mild degree

In adults, BMI < 18.5 kg/m2 or unintentional loss of weight (5-9%) with (moderate degree)

evidence of suboptimal intake resulting in moderate loss of subcutaneous fat and/or

moderate muscle wasting OR (mild degree) evidence of suboptimal intake resulting in mild

loss of subcutaneous fat and/or mild muscle wasting.

European Society for Clinical Nutrition and Metabolism (ESPEN) (2006)12

“A state resulting from lack of uptake or intake of nutrition leading to altered body

composition (decreased fat free mass (FFM) but specifically body cell mass (BCM) and

diminished function”.

American Society for Parenteral and Enteral Nutrition (ASPEN) (2005)13

“Any disorder of nutrition status, including disorders resulting from a deficiency of nutrient

intake, impaired nutrient metabolism, or overnutrition”

British Association for Parenteral and Enteral Nutrition (BAPEN) (2003)14

“A state of nutrition in which a deficiency, excess or imbalance of energy, protein, and other

nutrients causes measurable adverse effects on tissue (shape, size, composition), function

and clinical outcome.”

There is inconsistency in how the terms “nutrition screening” and “nutrition

assessment” are defined. They are often used interchangeably in the literature, and

tools that measure nutritional status are often described as either screening or

assessment tools. Both terms should be well differentiated as their applications have

distinct clinical implications for follow-up actions, interventions and the monitoring

plan. In this thesis, the following definitions are adopted:


8

Nutrition Screening

Nutrition screening is a critical antecedent step in the Nutrition Care Process15.

Nutrition screening is the process of identifying characteristics known to be associated

with nutrition problems16. Its purpose is to pinpoint malnourished individuals or those

at nutritional risk who may benefit from nutrition care. A nutrition screen may be

completed by a dietitian, dietetic technician, nurse, physician, dietary manager, or

other qualified health care professional. It should provide access to timely nutrition

care. Nutrition assessment takes place after screening occurs in those identified as at

risk, followed by nutrition diagnosis, intervention, monitoring and evaluation15.

The screening process has the following characteristics16:

• Facilitates completion of early intervention goals

• Includes the collection of relevant data on risk factors and the interpretation of

data for assessment, intervention, and treatment

• Determines the need for a nutrition assessment

• Is cost-effective


9

Nutrition assessment

Nutrition assessment is the first step of the Nutrition Care Process initiated by referral

or screening15. It is a comprehensive approach, completed by a dietitian, to define

nutritional status that involves a systematic process of obtaining and interpreting any

combination of medical, nutrition, medication histories, physical examination,

anthropometric measurements, and laboratory data13, 16. Nutrition assessment provides

a foundation for the nutrition diagnosis at the next step of the Nutrition Care Process,

followed by intervention15. Because of the inextricable relationship between

malnutrition and severity of illness, and tools of nutrition assessment often reflect

both nutritional status and severity of underlying disease. An assessed state of

malnutrition or presence of specific indicators of malnutrition could in fact refer to the

consequences of a combination of an underlying illness and associated nutrition

changes and deficits13, 16.


10

Figure 1-1: Relationships between the key concepts in the research study

Predict

Incorporated into

Influence variations

Different combination of indices/tools

Variety of nutrition indices and tools

Confounding factors for


Consequences/Clinical

outcomes

Prevalence

Nutrition screening

Risk factors

Identify patients at risk of malnutrition

Predict

• Age • Country • Setting • Medical

diagnosis

Malnutrition

Variety of screening tools

• Factors related to ageing

• Factors related to hospitalisation

Nutrition intervention

Nutrition monitoring and evaluation


11

1.3 Literature search strategy

The literature selected for review in this thesis was identified through several search

strategies. Appropriate search terms and methodology relating to malnutrition, clinical

outcomes, nutritional assessment, and nutritional screening in older adults were used

for the Medline and CINAHL databases via the EBSCO host search engine from

inception till October 2005 for the initial literature search in developing the research

proposal (Appendix Table A-37). Additional searches were performed using the same

search terms from October 2005 to June 2010 to include any new references. From

the initial lists of references, cross reference hand searches were also done to retrieve

relevant references which did not appear in the databases searched previously.

All searches were limited to the English language. Depending on the sections within

the literature, different criteria were used to select the studies reviewed. In the review

of the malnutrition prevalence (section 1.5), only studies which were conducted in

older adults, and had specified the malnutrition assessment method, frequency and

prevalence were included. In reviewing the literature on changes in nutritional status

during hospitalisation (section 1.5.4), only studies conducted in the hospital acute care

setting were included regardless of patient age. They must also specifically define the

change in nutritional status and its prevalence. In the review of the malnutrition

consequences (section 1.6), studies reviewed were limited to longitudinal studies

conducted in hospitalised older adults (aged >60 years) which documented the impact

of malnutrition on clinical outcomes.


12

1.4 Risk factors for malnutrition

Old age is one of the main risk factors for malnutrition17-22. Malnutrition is not an

inevitable side effect of ageing, but many factors associated with the process of

ageing could reduce the appetite, influence the nutrient intake and promote

malnutrition. These risk factors are shown in Table 1-2. Malnutrition usually arises

during periods of increased energy needs such as infections and sepsis. Older adults

are particularly at higher risk of malnutrition because of their decreased nutritional

reserves, multiple underlying chronic medical conditions and co-morbidities, and the

effect of repeated acute ill health23. They are also more likely to lose their appetite24

and develop malnutrition with less physiologic stress and within a much shorter

period as compared to younger adults25. Therefore it is expected that sicker older

adults who require hospital admission have poorer nutritional status than those

relatively healthier ones living in the community.

During hospitalisation, some of the hospital practices may further increase the risks of

malnutrition or cause further nutritional depletion in the older patients8. Table 1-3 lists

the malnutrition risks associated with hospitalisation. With the numerous malnutrition

risk factors associated with ageing, compounded with the state of the acutely ill

patients and the hospitalisation-associated risks, older hospitalised patients are more

likely to experience reduced nutritional status. These factors interact to limit dietary

intake, increase nutritional needs or nutrient losses, and lead to nutritional decline26,

hence increasing the risk of complications27. Theoretically, the influence of ill health

on the nutritional status of hospitalised patients may be limited to the time of acute

illness. When they recover, the nutrition status should improve. However, correcting

the malnourished state may be more difficult in older than in younger adults and less


13

reversible28, 29. Therefore it is important for these older patients to receive closer

nutrition attention before, during and after hospital admission to maintain or prevent

deterioration of their nutritional status.

Table 1-2: Risk factors for malnutrition among older adults (5, 30)

Clinical factors Psychosocial factors Physical factors

• Anorexia (poor appetite)31, 32

• Loss of taste and smell33

• Dysphagia (swallowing

difficulty) 34

• Chronic medical conditions

and comorbidities

• Hospitalisation

• Disease and disability e.g.

Parkinson’s disease,

malabsorption

• Polypharmacy and

medication side effects such

as nausea, vomiting,

anorexia, and altered taste

perception 35

• Depression36

• Cognitive impairment

(Dementia, Delirium) 31,

37

• Anxiety

• Bereavement

• Social isolation (living

alone) 34, 38-40

• Poverty

• Low educational level

• Lack of nutrition

knowledge about food

and cooking

• Declined functional

status e.g. decreased

mobility, ability to shop,

prepare and self-feed 32,

38, 40-44

• Oral and dental

problems e.g. mouth

pain, poor dentition, ill-

fitting dentures, dry

mouth 19, 31, 33, 35, 42, 45-49


14

Table 1-3: Malnutrition risks associated with hospitalisation 34

• Length of stay 50

• Acute disease states e.g. sepsis, infection

• Severity of illness

• Multiple chronic diseases and comorbidities 20, 50, 51

• Fatigue, pain

• Anorexia, early satiety

• Medication side effects

• Immobility

• “Nothing by mouth” (NPO) order

• Interruption and withholding of meals

• Failure to observe patients’ food intake and to recognise increased nutrition needs

• Delay of nutrition support

• Lack of flexibility in hospital catering and poor diet provided by hospital 52, 53

• Inadequate nutritional care54

Although the risks associated with malnutrition in the elderly have been well reported

in Caucasian populations5, 30, it is unclear if similar characteristics persist across

different cultural and care settings. There are limited data available for Asian

populations.

It was shown in a recent cross-sectional study with 2605 Chinese community-

dwelling older adults (mean aged 66.0+7.7 years) in Singapore that being male (OR

1.29, 95% CI [1.05-1.57]), single, divorced or widowed (OR 1.46, 95% CI [1.15-

1.84]), or living alone (OR 2.05, 95% CI [1.43-2.94]) were independently associated

with higher nutritional risk (using DETERMINE checklist55 score >3)56. Thirty per

cent were identified with nutritional risk, and this group was more likely to have more

comorbid medical conditions (OR 3.14, 95% CI [2.11-4.69]), be hospitalised (OR


15

2.24, 95% CI [1.49-3.36]), be functionally dependent (OR 1.72, 95% CI [1.41-2.11]),

report poorer quality of life (OR 2.01, 95% CI [1.67-2.42]) and be depressed (OR

1.81, 95% CI [1.42-2.31]). Likewise, a Hong Kong study of 2011 community-

dwelling elderly Chinese aged >70 years, also found many of these factors were

associated with lower anthropometric values57.

Both studies were conducted with older adults in the community. However, none of

the studies reviewed had characterised the profile or identified the risk factors

associated with elderly malnutrition upon hospital admission in an Asian context. It is

not known if there are any unique risk factors specific to the Asian population.

It is evident that older adults are more susceptible to developing malnutrition due to

the multitude of risk factors discussed in this section. Besides that, it is also important

to understand the magnitude and impact of malnutrition in older adults. These will be

reviewed in sections 1.5 and 1.6.


16

1.5 Prevalence of malnutrition

1.5.1 Malnutrition prevalence in hospitalised patie nts

The existence of hospital malnutrition was first reported more than 30 years ago as a

common occurrence accompanying the stress of illnesses in patients 52. Hospital

malnutrition was described by Butterworth in 1974 as a “skeleton in the hospital

closet”, referring to the unrecognised and untreated malnutrition in many hospitals

then58. He used the term “iatrogenic malnutrition” (“physician-induced malnutrition”)

to suggest that the nutritional aspects of patients were often overlooked and neglected

during hospitalisation.

In the recent decade, malnutrition remained a common problem for hospitalised

patients with various diagnoses. Table 1-4 shows the wide range of malnutrition

assessment methods used and their reported prevalence. A prevalence ranging from

5% to 79% was shown in different countries using various definition of malnutrition

(Table 1-4). Besides the different nutrition assessment indices and tools applied in the

studies, varying age distribution of the hospital samples, the different patient-mix,

countries and settings are other contributing factors to the wide prevalence range

observed.

In one of the earlier large prospective studies, McWhirter and Pennington (1994)59

recruited 500 patients (aged 16-64 years) from five different clinical disciplines

(including geriatric medicine) in a UK hospital. They reported a high prevalence of

malnutrition (overall 40%; geriatrics 43%) more than 15 years after it was first

reported by Butterworth58. Patients were considered malnourished when BMI was


17

<20kg/m2, and TSF or MAC was <15th percentile. Unintentional weight loss >10% in

the 6 months preceding admission was used as additional evidence of malnutrition.

A study conducted by Raja et al60 at TTSH reported an overall admission malnutrition

prevalence of 15% defined using the SGA amongst 658 medical and surgical adult

patients (mean age 56 years). The prevalence was about 23% among the medical

patients and old age was shown to be predictive of malnutrition occurrence (OR 1.05,

95% CI not reported, p<0.01)60. The lower prevalence rate reported in the Singapore

study could be related to differences in the sample population and malnutrition

assessment method used in other studies59.


18

Table 1-4: List of common malnutrition assessment methods used in studies and their associated range of malnutrition prevalence

Nutrition parameters Assessment methods Malnutrition prevalence (%)

Anthropometry Weight (% IBW) <80; <85; <90 14-3261-63 Weight loss >10% in 3 months; >10% in 6 months;

>5% in 3 months; >5% in 6 months 6-4018, 64, 65

Weight/height (%) <80; <90 22-5566, 67 BMI (kg/m2) <18;<18.5;<19;<20;<22;<23 9-5919, 68-78 MAC (centile) <5th;<10th;<15th;<25th 12-3669, 79 TSF (mm) <9,<12 (women); <5,<6 (men)

<90%; <15th centile 17-7669, 76, 80

MAMC (centile) (cm)

<15th centile, ;<90%; <18,<19 (women); <20,<21,<23 (men)

20-5376, 80

CAMA (cm2) <16.9,<21.4(women); <16,<21.6 (men); <5th centile

20-3679, 81, 82

Biochemical ALB (g/dL) <30;<35;<36 15-5368, 83, 84 TLC (mm-3) <800;<1000;<1500 21-3163, 68 Multi-factorial tool MNA (score) <17;<23.5 5-7114, 27, 38, 69, 73,

76-81 SGA (rating) B, C 13-7921, 22, 50, 51, 60,

71, 85-95 NRI 14-6896-98 Combination of criteria

Combination of two or more anthropometrical and/or biochemical nutrition parameters

14-6159, 99-106

Abbreviations: ALB: serum albumin; BMI: body mass index; CAMA: corrected arm muscle area; IBW: ideal body weight; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; MNA: Min Nutritional Assessment; NRI: Nutrition Risk Index; SGA: Subjective Global Assessment; TLC: total lymphocyte count; TSF: triceps skinfold thickness


19

1.5.2 Malnutrition prevalence in younger versus old er hospitalised

adults

Old age has been shown to be a significant predictor for malnutrition17, 21, 22 due to the

numerous malnutrition risk factors associated with ageing that have been discussed in

section 1.2. Table 1-5 shows the studies that have reported higher prevalence of

malnutrition among older than younger hospitalised patients. This difference in

prevalence could be twice as high in the older than in the younger patients (30% vs

13%)51. Comparison between studies also showed higher prevalence rates among

older patients than younger patients71, 91, 107.

Table 1-5: Comparison of malnutrition (defined by SGA) prevalence between younger and older hospitalised adults

Author/Year/Citation/Country Sample size Prevalence (%)

<65 years

Prevalence (%)

>65 years

Barreto Penie, 200592, Cuba 1905 41a 56b

Kyle et al, 200284, Switzerland 995 50 79

Middleton et al, 200187, Australia 819 27 a 43b

Pirlich et al, 200551, Germany 794 13 30

Waitzberg et al, 200150, Brazil 4000 45 53

Abbreviations: SGA: Subjective Global Assessment

a: <60 years; b: >60 years


20

1.5.3 Variations in malnutrition prevalence among o lder adults

Care settings

Malnutrition in the elderly has been widely reported, with most of the studies

conducted in the US and Europe. These studies are summarised in Tables 1-6 and 1-7,

and showed prevalence ranging from 5% to 79% across various care settings using

varying assessment methods of malnutrition. Malnutrition ranged from 12-79% in

hospitalised elderly patients, 6-55% in sub-acute care/rehabilitation, 5-21% in the

community, and 25-71% in long-term care (Tables 1-6 and 1-7). In general, it is

higher among older adults in the hospital and long term care settings. The presence of

comorbidities and onset of acute illnesses with increased nutritional needs in the

hospitalised patients could be contributing factors for the higher prevalence observed

in older hospitalised adults5, 108.

Malnutrition assessment methods

Wide variations in malnutrition prevalence were observed among older adults within

the same care settings, especially in the acute hospital care setting (12-79%) (Tables

1-6 and 1-7). This observation may mainly be attributable to the lack of a standardised

definition for malnutrition or it has been poorly defined. The malnutrition assessments

ranged from different anthropometric and laboratory measures and cut-offs, to various

nutrition assessment tools used (Table 1-4). Studies either did not specify how

nutrition variables were interpreted109, or adopted subjective clinical judgment with

limited use of nutrition parameters to diagnose malnutrition110. Some studies even

classified those patients identified as “at risk of malnutrition” as malnourished

patients109, 111, hence probably over-estimating the prevalence of malnutrition. In view

of these limitations, the Dietitians Association of Australia (DAA) recently developed


21

an evidence based practice guideline for nutritional management of malnutrition in

adult patients112, and recommended specific criteria and use of nutrition assessment

tools in the various care settings.

Variation in malnutrition prevalence within the same study sample using different

malnutrition assessment methods has also been reported. Kyle et al (2002)71 showed

that the prevalence could range from 14% (BMI<20kg/m2) to 61% (SGA) when

different nutrition assessment indices and tools were applied to 392 hospitalised Swiss

elderly (>60 years). Likewise a study with 154 nursing home (similar to residential

care facilities in Australia) residents (mean age 77+12 years) in Singapore, showed a

different malnutrition prevalence of 39% and 59% when MNA (score <17) and BMI

(<18.5kg/m2) were used respectively78.

When a common assessment method was selected for comparison of malnutrition

prevalence in hospitalised older adults, a narrower range was observed (MNA: 19-

35%; SGA 41-63%) (Table 1-6 and 1-7). The MNA (score <17) generally detected a

lower malnutrition prevalence compared to the SGA in acute care settings. It is

possible that the MNA (score <17) could potentially underestimate the malnutrition

prevalence in acute care settings compared to the SGA. This was shown by Persson et

al113 in their study where 83 patients (mean age 83+7 years) were assessed using both

the MNA (<17: 26%) and SGA (rating B+C: 63%).

Participants’ characteristics

Differences in socio-demographic and clinical characteristics of the study samples

may account for some of these variations observed. Some studies included patients


22

from geriatric units79, some included only demented patients74, 114, while others

included elderly patients from various disciplines i.e. stroke69, general

medical/surgical patients62, 63, 99. The clinical characteristics and implications

associated with these various patient groups could predispose them to different levels

of nutrition risks.

Despite the issues around malnutrition assessment methods and the wide range of

malnutrition prevalence reported, malnutrition is clearly prevalent among hospitalised

patients and especially among the older adults. This highlights the importance of

preventing, evaluating and treating malnutrition in this population. With the growing

elderly populations in the world, malnutrition in the elderly will have significant

implications in many countries, including Singapore. Therefore, it would be useful if

a standardised malnutrition assessment method could be determined for more accurate

comparisons and surveillance of malnutrition prevalence between and within

countries and settings.


23

Table 1-6: Prevalence of malnutrition in hospitalised older adults (>60 years) from acute care

Reference/Country Sample (n)

Age^ (years)

Malnutrition assessment method Prevalence (%)

Hospital Mean+SD

Vanderwee et al, 201033; Belgium 2329 84+5 MNA<17 33

Volkert et al, 2010115; Germany 205 83+5 SGA: rating B +C 60 Feldblum et al, 2009116; Israel 204 >65 MNA<24 39

Lei et al, 2009117; China 184 68+6 MNA<17 20

Oliveira et al, 200944; Brazil 240 >60 MNA<17 29

Feldblum et al, 200731; Israel 259 >65 MNA<17 19

Coelho et al, 2006118; Brazil 197 >60 BMI<18.5kg/m2 30

Pirlich et al, 2006119; Germany 306 >65 SGA: rating B +C 56

Faxen-Irving et al, 200574; Sweden 231 80+7 BMI <23kg/m2 52

Paillaud et al, 200570; France 185 82+1 BMI < 20kg/m2 30

Kruizenga et al,200365; Netherlands 6150 63+16 MN: involuntary weight loss >10% in 6 month; At risk: weight loss 5-10%

13

Kyle et al,200271; Switzerland 392 >60 SGA: rating B +C BMI<20kg/m2

79 14

Persson et al, 2002113; Sweden 83 83+7 SGA: rating B +C MNA <17

63 26

Van Ness et al, 2001120; Switzerland 1145 84+7 MNA<17 19

Gazotti et al, 2000121; Belgium 175 78+9 MNA<17 22

Azad et al, 1999109; Canada 160 >65 detailed nutrition assessment: weight, height, weight loss, TLC, ALB, cholesterol, risk factors MN, diet intake

15

Covinsky et al, 199994; US 369 >70 SGA: rating B +C 41

Compan et al, 199920; France 299 83+7 MNA<17 25

Gariballa et al, 199869; UK 201 78+9 MAC< 25th centile BMI<20kg/m2 ALB<35g/L

12 31 19

Ek et al, 199693; Sweden 90 >70 SGA: rating B +C 48

Potter el al, 199581; UK 69 69-96 CAMA <16 cm2 (men); <16.9 cm2 (women)

26

Burns & Jensen, 199563; US 268 78+8 Severe MN: ALB <30g/L or TLC <1000/mm3, or weight <85% IBW

31

Cederholm et al, 199599; Sweden 205 75+1 at least 3 of 5 criteria: weight index, TSF, MAMC, ALB, delayed hypersensitivity

20

Muhlethaler et al, 199579; Switzerland

219 >65 ALB < 30g/L, Prealbumin, transferring CAMA <5th centile

28 36

Mowe et al, 199476; Norway 311 >70 BMI<20kg/m2 (men); <19.2kg/m2 (women) ALB <35g/L

29 38

Lansey, 1993122; US 47 86+6 ALB, TLC + <90% IBW or any 2 (MAC, TSF, MAMC, SSF, BMI) <5th centile

36

Constans et al, 1992123; France 324 >70 Mod: MAC <10th centile or ALB <3.5g/dL; Severe: both

62(women) 46(men)

Volkert et al, 1992110; Germany 300 >75 Clinical judgement by physicians (physical)

22

Abbreviations: ALB: serum albumin; BMI: body mass index; BW: body weight; CAMA: corrected arm muscle area; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; MN: malnourished; MNA: Mini Nutritional Assessment; mod: moderate; SGA: Subjective Global Assessment; TLC: total lymphocyte count; TSF: triceps skinfold thickness; wt/ht: weight for height ratio ^ Age range is presented when mean age+SD was not reported


24

Table 1-7: Prevalence of malnutrition in older adults (>60 years) in sub-acute, community and long term care settings

Reference Sample (n)

Age^ (years)

Malnutrition Assessment method Prevalence (%)

Sub-acute care /Rehabilitation Mean+SD

Brantervik et al, 200539 Sweden

294 83+8(F) 78+10(M)

At risk; BMI <22kg/m2, and/or weight loss>5% in 6 months

55

Neumann et al,200582 Australia

133 >65 MNA <24 BMI<22kg/m2 CAMA <21.6cm2(men); <21.4cm2(women)

6 17 20

Shum et al, 200575 Hong Kong

120 80+7 BMI< 18.5kg/m2 and ALB <35g/L 17

Thomas et al, 200283 US

837 76+13 ALB <35g/L MNA<17 BMI<19kg/m2

53 29 18

Community

Hsieh et al, 2010124; Taiwan 360 >65 MNA<17 5

Tsai et al, 2009125; Taiwan 301 >65 MNA<24 17

De Marchi et al, 200849; Brazil 471 >60 MNA<24 21

Kruizenga et al, 200365; Netherlands

533 59+20 MN: involuntary weight loss >10% in 6/12; At risk: weight loss 5-10%

6

Visvanathan et al, 2003126; Australia

250 67-99 MNA<17 5

Stookey et al, 200073; China 3800 >60 BMI < 18.5kg/m2 16

Edington et al, 199672; UK 301 >65 as per McWhirter et al, 1994 9

Posner et al, 199419; US 1156 >70 BMI <22kg/m2 16

de Groot et al, 1991127; Europe 2332 NR BMI <20kg/m2 15

Long term care

Chan et al, 201078; Singapore 154 77+12 MNA<17 BMI<18.5kg/m2

39 52

Banks et al, 200717; Australia 458 79+12 SGA: rating B +C 49

Wikby et al, 2006102; Sweden 127 >65 At least 2 (1 biochem, 1 anthro) : Wt/ht (Ratio <80%) MAC, TSF (M<6, F<12), MAMC (M <23 for <79yrs,<21 for >79 yrs; F<19 for <79 yrs, <18 for >79 yrs) ALB (<36), Prealbumin (<0.23)

32

Crogan & Pasvogel, 2003128; US 311 >65 BMI <22kg/m2 39

Christensson et al, 2002129; Sweden 261 >65 SGA: rating B +C MNA<24

53 79

Sacks et al, 200095; US 53 >65 SGA: rating B +C 70

Saletti et al, 2000130; Sweden 166 84+8 MNA<17 71

Compan et al, 199920; France 423 83+10 MNA<17 25

Keller, 1993106; Canada 200 >65 Wt/ht, TSF, subscapular skinfold, MAC,MAMC,MAMA <25th centile; BMI <20; weight loss >10% in 6 months

46

Larsson et al, 1990101; Sweden 501 >65 3 or more criteria: Wt index <80%, TSF, MAMC, Prealbumin, ALB <36, skin test

29

Abbreviations: ALB: serum albumin; BMI: body mass index; BW: body weight; CAMA: corrected arm muscle area; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; MAMA: mid-arm muscle area; MN: malnourished; MNA: Mini Nutritional Assessment; MNA-SF: Min-Nutritional Assessment-Short Form; mod: moderate; NR: not reported; NRI: Nutrition Risk Index; SGA: Subjective Global Assessment; TLC: total lymphocyte count; TSF: triceps skinfold thickness; wt/ht: weight for height ratio ^ Age range is presented when mean age+SD was not reported


25

1.5.4 Under-diagnosed hospital malnutrition

Malnutrition prevalence has been widely reported as shown in sections 1.5.1 to 1.5.3.

However, it is frequently under-recognised, under-diagnosed and under-treated in

hospitalised patients59, 87, 90, 105, 131 even until recent years132, 133.

A recent study of 100 elderly patients (mean age 81.9+6.3 years) from a large

Australian tertiary hospital, showed that 30% were malnourished and 61% were at

risk of malnutrition (defined by MNA)132. More than 95% of the patients presenting

with weight loss or loss of appetite were either at risk of malnutrition or

malnourished. Yet medical and nursing professionals identified only 19% with weight

loss and 53% with loss of appetite; and only 7% and 9% were referred to a dietitian

respectively132. Gout et al133 also reported poor recognition of malnutrition in another

large tertiary hospital among 275 adult patients (mean age 59.5+19.9 years) where

23% were found to be malnourished (defined by SGA)133. A dietitian was involved in

45% malnourished cases, and 29% were correctly documented as malnourished in the

medical notes133.

In the earlier Singapore study conducted at TTSH60, malnutrition was also similarly

under-diagnosed and under-treated with only 17 (16%) of the 105 malnourished

patients being referred to a dietitian for nutrition interventions by the treating

physician, and only one of these patients having the diagnosis of malnutrition

documented.

However, there was a trend towards an improvement of hospital malnutrition

management in the recent years, particularly in the UK105, 134. In a recent large cross-


26

sectional study by O’Flynn et al (2005)135 in a UK hospital (n=2283, >16 years),

referral to a dietitian for nutritional support was increased from 57% to 71% over a 5

year period after implementation of nutrition care strategies such as implementation

of a nutrition screening tool and improved catering services and nutrition education

provision. Although these recent dietitian referral rates in UK showed more than a 10-

fold increase compared to that reported by McWhirter and Pennington in 199459 (5%),

malnutrition is still not adequately recognised and treated in hospitalised patients50, 59,

87, 90, 105, 115.

There are several reasons that might explain the sub-optimal level of malnutrition

recognition evident across different countries. Firstly, there may be poor systems of

nutritional measurements and documentation. Often limited nutritional information

was available for assessment of hospitalised patients, where more than three-quarters

of patients could not have their nutrition status assessed due to inadequate data

(anthropometry or biochemical) available from routine assessment136. Nutritional

information was also often not documented in up to half of the malnourished

patients50, 59, 132, and less than one-third of the malnourished patients were diagnosed

as malnourished either on admission or discharge59, 66, 136.

Secondly, there might have been a lack of good practice guidelines for recognising

and initiating nutrition therapy as the literature reveals that more than two-thirds of

malnourished patients did not receive any form of nutrition support therapy136, or

were not routinely referred to dietitians 59, 64, 132. This might be related to the limited

training and knowledge of doctors and nurses, and tendency to mainly focus on the


27

patients presenting medical conditions and give less credence to other important

components of care such as nutritional status86, 94, 132,


28

1.5.5 Change in nutritional status during hospitali sation

With the poor recognition and treatment of malnutrition upon hospital admission,

further nutritional depletion could occur and lead to worsened nutritional status during

hospitalisation. Patients who are well-nourished or at risk of malnutrition may

experience a decline in nutritional status and develop malnutrition due to the risks

associated with hospitalisation mentioned earlier in section 1.2 (Table 1-3).

Documentation and monitoring of nutritional status during hospitalisation have not

been as widely reported as the prevalence of admission malnutrition. From the review

in this thesis, the change in nutritional status has been reported in only nine studies in

Europe and US summarised in Table 1-8. Among them, only five were conducted in

older adults. In general, these studies (Table 1-8) showed that 16-83% of patients

declined in their nutritional status during hospitalisation, regardless of their admission

nutrition status and the duration of hospital stay (range from 12 days to 26 weeks).

The only exception was the study in France (n=495, age 55-103 years) that showed

49% of malnourished patients in acute and sub-acute care had improved nutritional

status through increased MNA score20. Those who stayed longer had a greater

improvement in the MNA score and this could be explained by questions in MNA

which relate to acute disease and treatments that tend to improve with time20.

No standard nutrition parameters were used in monitoring the change in nutritional

status during hospital admission in these studies (Table 1-8), just as there was a lack

of a standard malnutrition assessment method. Anthropometric measurements (weight

and arm measurements) appeared to be the most commonly adopted approach to

monitor patients’ nutritional status during admission. Half these studies used weight


29

measures and weight loss as an indication of deterioration in nutritional status59, 69, 88,

137 and showed mean percentage weight loss (of baseline admission weight) was 1.8-

5.4% over an average LOS of two weeks. The different nutrition parameters used in

monitoring the nutritional status in these studies could have contributed to the

inconsistency observed in detecting decline in nutritional status during hospitalisation.

Appropriately identifying patients who have deteriorated nutritional status is as

important as the identification of patients who are malnourished or at risk of

malnutrition on admission. Braunschweig et al (2000)88 reported that among 404

hospitalised adults (mean age 54+1 years), well-nourished patients on admission were

as likely or even more likely to experience decline in nutritional status (38%) as those

who were moderately malnourished (20%) or severely malnourished (33%). This

study showed decline in nutritional status was associated with poorer clinical

outcomes such as higher hospital costs and higher risks of complications. The

predictive ability of declined nutritional status on clinical outcomes in hospitalised

adults has not been as widely studied as the predictive ability of admission

malnutrition.

Overall, there was no standardised approach identified in the literature to define and

assess malnutrition. The prevalence of malnutrition was higher in older than younger

hospitalised adults. The wide variations in the malnutrition prevalence reported could

be attributed to the differences in care settings, malnutrition assessment methods, and

patient characteristics between studies. Despite the reported high prevalence of

hospital malnutrition, it remained under-recognised, under-diagnosed, and under-

treated. The poor recognition and treatment of malnutrition during hospital admission


30

could lead to further nutritional decline. The monitoring of nutritional status during

hospitalisation has not been studied as extensively and there was also a lack of a

standard assessment approach to monitor its change.


31

Table 1-8: Studies which examined changes in nutritional status during hospitalisation

Author/Year/ Citation/Country

Sample (N)*

Population/Setting

Age^ Mean+SD

(years)

Admission# malnutrition

(%)

Assessment method of

change

Change in nutrition status during admission Duration of follow-up from admission to

discharge (ALOS) Braunschiweig et al, 200088; US

404

Adults Hospital

54+1 54 (SGA)

SGA and weight loss

59% malnourished at discharge; 31% decline (in well-nourished:38%; moderately

malnourished:20%; severely malnourished: 33%)

17 days

Corish et al, 2000137; UK

594 (202)

Adults Hospital

16-64 11+

Weight loss Weight loss: 43% of malnourished; 66% of normal weight

12 days

Compan et al, 199920; France

495 Elderly Acute/

Subacute

55-103 25

MNA 49% of malnourished improved MNA score: +1.5 points (acute); +3 points

(subacute)

Acute:12 days Subacute: 41 days

Bruun et al, 1999138; Norway

244 (64)

Surgical Hospital

22-92 39^

Weight loss 50% weight loss <5%; 25% weight loss 5-10%; 8% weight loss 10-15%

Median LOS 14 days (Range 3-115 days)

Gariballa et al, 199869; UK

201 (96)

Elderly Hospital

78+9 31 (BMI<20kg/m2)

Weight loss

64% lost weight; Mean weight loss:1.8% (women);2.4% (men)

At 2 weeks of admission

Antonelli Incalzi et al, 1996139; Italy

302

Elderly Hospital

79+6 16(men) 28(women)

(BMI<22kg/m2)

MAMC 64% reduced MAMC ALOS not reported

McWhirter & Pennington, 199459; UK

500 (112)

Adults Hospital

>16 40+ Weight loss Weight loss: 75% of malnourished; 39% of well-nourished; mean weight loss: 5.4%

ALOS not reported

Constans et al, 1992123; France

324 (53)

Elderly Hospital

>70 46(men) 62(women)

MAC;ALB Decline in MAC and ALB in all 53 patients At 15th day of admission

Larsson et al, 1990101; Sweden

501

Elderly Long-term

care

>65 29 As per criteria for

malnutrition

26% (n=182) well-nourished developed malnutrition after 2 weeks

At 8 weeks and 26 weeks

Abbreviations: ALB: serum albumin; ALOS: average length of stay; MAMC: mid-arm muscle circumference; MAC: mid-arm circumference; MNA: Mini Nutritional Assessment; SGA: Subjective Global Assessment; TSF: triceps skinfold thickness; *Numbers in brackets indicate the sample assessed at follow-up/discharge;.^ Age range is presented when mean age was not reported in the studies. #Refer to Table 1-6 for malnutrition assessment methods in these studies; ^ Defined by BMI<20kg/m2 +weight loss>5% 3-months preceding admission; + Defined by BMI <20kg/m2 + TSF/MAMC <15thcentile, weight loss >10% in 6-months


32

1.6 Consequences of malnutrition

It is well established that malnourished patients have a higher risk of developing

adverse clinical outcomes and increased healthcare costs as reported in the review by

Stratton et al (2003)7. Some of the more common adverse clinical consequences of

malnutrition are in Table 1-9. Malnutrition is an important predictor of mortality and

morbidity in hospitalised patients 38, 140. Among hospitalised older adults, malnutrition

remained a significant predictor for adverse clinical outcomes even when illness

severity, comorbidities or functional dependence on admission were controlled for 94.

The strong association between poor nutritional status and poor clinical outcomes was

evident regardless of age 140, care setting 82, 105, 126, clinical discipline61, 69, 89, 141, 142 and

country87, 89, 105, 143. However the impact of hospital malnutrition on clinical outcomes

was more severe among the elderly hospitalised patients 91, 103.

Table 1-9: Common adverse clinical consequences of malnutrition 7

1. Increased mortality 61, 62, 69, 141

2. Development of infections 61, 81, 99, 104

3. Decline in functional status 82, 144-147

4. Development of pressure ulcers 148, 149

5. Prolonged hospital stay 18, 82, 87, 91, 105, 150

6. Discharge to higher level of care 82, 150

7. Hospital readmissions 76, 85, 151, 152

8. Increased healthcare costs 140, 150, 153, 154

9. Reduced quality of life 82, 128


33

Table 1-10 lists the 15 longitudinal studies (n=83 to n=2192) which have reported the

impact of malnutrition on clinical outcomes i.e. mortality, LOS, readmission,

functional status, and discharge to higher level care, among hospitalised older adults

without adjustment for potential confounders. These studies were mainly conducted in

the US and Europe with only one Asian study. Follow-up periods ranged from the

mean hospital LOS of nine days to three years post-discharge.

In addition, Table 1-11 lists the seven longitudinal studies (n=92 to n=1145) which

reported the impact of malnutrition on clinical outcomes (i.e. mortality, LOS,

functional status, readmission, and discharge to higher level care), among hospitalised

older adults with adjustment for the established covariates (i.e. age, gender, functional

status, cognition, and comorbidities). All these studies were conducted in the US and

Europe. Follow-up periods ranged from the mean hospital LOS of 15 days to seven

years post-discharge.

The commonly reported clinical outcomes amongst hospitalised older adults are

discussed in sections 1.6.1 to 1.6.6 with reference to the studies summarised in Tables

1-10 and 1-11.


34

Table 1-10: Longitudinal studies of the impact of malnutrition on clinical outcomes in hospitalised elderly without adjustment for covariates

Author/Year/Citation

Country Follow-up period

Setting (N) Mean+SD Age^ (years)

Malnutrition assessment method

Outcomes/Effect size

Feldblum et al, 2009116

Israel 3 months Hospital (n=204)

>65 MNA <24 LOS: 7.1 vs 5.0 days; Hospital readmission days at 3-month: 2.8 vs 1.4 days

Lang et al, 2006111

France mean LOS 20 days

Hospital (n=908)

84+6 MNA-SF<12 LOS >30 days: OR 2.5, 95% CI (1.7-19.6)

Pirlich et al, 2006119

Germany NR Hospital (n=2192)

62+17

SGA rating: B+C LOS: difference 4.6 days (42% increase)

Kagansky et al, 200537

Israel 2.7 years Hospital (n=414)

>75 MNA<17 LOS: 60 vs 28 days; Hospital mortality:39% vs 13%

Neumann et al, 200582

Australia 90 days Rehabilitation (n=133)

>65 MNA <24 CAMA <21.6 (men); <21.4 (women) BMI <22kg/m2

#LOS: 19 vs 14 days; Discharge to higher level care: OR 2.3, 95% CI (1.1-4.8); Functional status at 90 days: MBI 85+19 vs 96+7

Shum et al, 200575

Hong Kong

mean LOS 14 days

Hospital (n=120)

80+7 BMI<18.5kg/m2 ALB<35g/L

Hospital mortality: 25% vs 4%

Kyle et al, 200496

Germany mean LOS 10 days

Medical Hospital (n=1273)

>60 NRI<98 ALB<35g/L

LOS >11days: OR 2.2(mod); OR 3.5 (severe)

Persson et al, 2002113

Sweden 3 years Hospital (n=83)

83+7 SGA rating: B+C MNA<24

Mortality 1 year: 40% vs 20%; 3 year: 80% vs 50%

Abbreviations: ALB: serum albumin; BMI: body mass index; CI: confidence interval; IBW: ideal body weight; LOS: length of stay; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; MBI: Modified Barthel Index; MNA: Mini Nutritional Assessment; MNA-SF: Mini Nutritional Assessment-Short Form; NR: not reported; NRI: Nutrition Risk Index; SGA: Subjective Global Assessment; TLC: total lymphocyte count; TSF: triceps skinfold thickness; ^ Age range is presented when mean age was not reported in the studies. # Outcomes reported based on malnutrition assessment method MNA<24; functional status at 90 days was the only outcome adjusted for admission functional status


35

Table 1-10: Longitudinal studies of the impact of malnutrition on clinical outcomes in hospitalised elderly without adjustment for covariates (continued)



Setting (N)

Mean+SD Age^ (years)


Outcomes/Effect size

Van Nes et al, 2001120

Switzerland mean LOS 36 days

Hospital (n=1145)

84+7 MNA<17 Mortality inhospital:11.3% vs 3.7%

LOS: 42 vs 30.5 days Friedmann et al, 1997152

US 3 months Medical/ surgical Hospital (n=92)

>65 ALB<35g/L; Weight loss >10% in 6 months

Higher probability of readmission at 3-month: effect size not reported

Cederholm et al, 199599

Sweden 9 months Hospital (n=205)

75+1 Any 3: Weight<80% IBW; TSF/MAMC<10th percentile; ALB<36g/L; delayed hypersensitivity<9mm

Mortality 9-month: 41% vs 18%

Sullivan et al, 1995155

US 1 year Hospital (n=350)

58-102 SGA rating: B+C Mortality 1 year: 20%

Volkert et al, 1992156

Germany 18 months Hospital (n=300)

>75 Clinical judgement by physician examination (details not reported)

Mortality 3-month: 40% vs 15% Mortality 18 month: 72% vs 44%

Larsson et al, 1990101

Sweden mean LOS 26 weeks

Hospital (n=501)

>65 Any 3: Weight<80% IBW; TSF/MAMC<10th percentile; ALB<36g/L; delayed hypersensitivity<9mm

Hospital mortality: 37% vs 19%

Sullivan et al, 1989136

US mean LOS 9 days

Hospital (n=250)

>65 ALB<2.5g/dL TLC<0.8cells/mm3 BMI<5th percentile

LOS: 10 vs 8 days Hospital mortality: 5% vs 0%

Abbreviations: ALB: serum albumin; BMI: body mass index; CI: confidence interval; IBW: ideal body weight; LOS: length of stay; MAC: mid-arm circumference; MAMC: mid-arm muscle circumference; MNA: Mini Nutritional Assessment; MNA-SF: Mini Nutritional Assessment-Short Form; NR: not reported; NRI: Nutrition Risk Index; SGA: Subjective Global Assessment; TLC: total lymphocyte count; TSF: triceps skinfold thickness; ^ Age range is presented when mean age was not reported in the studies.


36

Table 1-11: Longitudinal studies of the impact of malnutrition on clinical outcomes in hospitalised elderly, adjusting for covariates



Setting (N)

Mean+SD Age^ (years)


Outcomes/Effect Size Covariates

Faxen-Irving et al, 200574

Sweden 7 year Hospital (n=231)

80+7 BMI<23kg/m2 Mortality 7 year: OR 3.0, 95% CI (1.3-6.7)

Age, gender, dementia, comorbidities

Flodin et al, 2000157

Sweden 1 year Hospital (n=532)

81+8 BMI<20kg/m2 Mortality 1 year: 48% vs 28% Age, gender, marital status, BMI, diagnosis

Alarcon et al, 1999158

Spain 6 months Hospital (n=353)

82+7 Not defined LOS: no effect; Mortality 6-month: OR 3.7, 95% CI (2.3-6.1); Discharge higher care: no effect

ADL, function disability, polypharmacy, pressure sores, cognitive impairment, low pension

Covinsky et al, 199994

US 1 year Hospital (n=369)

>70 SGA rating: B+C

#Mortality, at 3-month: OR 3.3, 95% CI (1.5-7.0); 1 year: OR 2.8, 95% CI (1.5-5.5); ADL at 3-month: OR 2.8, 95% CI (1.1-7.5); Discharge to nursing home at 1 year: OR 3.2, 95% CI (1.1-9.9)

Age, race, gender, living situation, APACHE, Charlson score, cancer, admission ADL, CHF, COPD or dementia

Muhlethaler et al, 199579

Switzerland 4.5 years Hospital (n=219)

>65 CAMA<5th percentile Weight <80% ALB<30g/L

Mortality 4.5 year: OR 1.8, 95% CI (1.3-2.6)

Physical function, cognitive function, renal function, living alone

Sullivan, 1992151

US 3 months Rehabilitation (n=110)

>65 ALB (at discharge, not defined)

Readmission 3-month: effect size not reported

Discharge ADL, dementia, subscapular skinfold thickness, home ownership, discharge gamma globulin

Agarwal et al, 198862

US mean LOS 15 days

Hospital (n=80)

>85 ALB<30g/L Mortality during admission Age, diagnosis

Abbreviations: ADL: activities of daily living; ALB: serum albumin; APACHE: acute physiology and chronic health evaluation; BMI: body mass index; CAMA: corrected arm muscle area; CHF: congestive heart failure; CI: confidence interval; COPD; chronic obstructive pulmonary disease; LOS: length of stay; SGA: Subjective Global Assessment. ^ Age range is presented when mean age was not reported in the studies. # Outcomes significant only in the severely malnourished group (SGA rating C)


37

1.6.1 Mortality

Mortality is an important clinical and health status outcome. Poor nutritional status,

regardless of the malnutrition assessment method, has been shown in various patient

groups such as surgical, medical, stroke and geriatric patients, and countries to be

significantly associated with increased mortality rates at different follow-up periods

ranging from in-hospital to up to seven years (Tables 1-10 and 1-11)61, 62, 69, 74, 94, 141.

Mortality is the most studied clinical outcome among hospitalised older adults in

relation to their nutritional status (Tables 1-10 and 1-11). Only half of these studies

shown in Tables 1-10 and 1-11 had mortality followed-up beyond hospitalisation.

After adjusting for age, mortality rates were up to six times higher among

malnourished than well-nourished older patients75. Malnourished older patients had

up to three times higher mortality risk at 3-months94 and at up to seven years74,

compared to the well-nourished older patients. Only six of these studies adjusted for

covariates62, 74, 79, 94, 157, 158 (Table 1-11), and malnutrition remained predictive of

mortality even after adjustments for illness severity, comorbidity and functional

status94. Serum albumin, BMI and weight loss were identified to be independent

predictors of mortality in many of these studies62, 69, 74, 155, 159.

The association between malnutrition in the hospitalised elderly and mortality has not

been as well-documented in the literature for Asian countries as it has for the US62, 94,

160 and Europe74, 99, 113, 120, 156, 157. There was only one recent study identified with 120

elderly hospitalised patients aged > 60 years (80.3+7.4 years) in a Hong Kong

rehabilitation hospital which showed that malnourished elderly patients had a six

times increased risk of death in hospital75, however no adjustment for covariates was


38

made. To date, there does not appear to be any data on mortality rates and

malnourished geriatric patients in an acute hospital setting in Singapore.

1.6.2 Length of stay (LOS)

Length of stay (LOS) is both a service and patient outcome which is easily collected.

Patients admitted with multiple medical issues are more likely to require longer

hospital stay. However differences in healthcare systems between countries may also

influence length of hospital stay independent of the clinical status of patients. For

example, LOS also varies in short term influenced by bed availability. From the

studies reviewed (Table 1-10), hospital LOS is the second most common clinical

outcome studied among hospitalised older adults in relation to their nutritional status

on admission. These studies from the US136, Europe96, 111, 119, 120 and Australia82

consistently reported longer LOS among those who were malnourished. The LOS for

older patients who were malnourished could be up to 43% longer than for those who

were well-nourished119. Malnourished older patients were also shown to experience

up to two and a half times increased risk of longer hospital stay111. The severity of

malnutrition also had an influence on the extent of prolonged hospital stay. For those

who were severely malnourished, up to three times increased risk of longer stay were

reported96. However, most of these studies did not adjust for covariates. Only one

study (n=353, mean age 82 years) adjusted for factors such as functional status,

cognitive impairment and polypharmacy, but no relationship between nutritional

status and LOS was detected158. This suggests that the association found between

malnutrition and LOS in the other studies could potentially be confounded by a range

of other factors.


39

1.6.3 Discharge to higher level care

Discharge destinations indirectly reflect the functional status of patients. Only three

studies explained the relationship between nutritional status and discharge to higher

level care among hospitalised older adults82, 94, 158. Neumann et al82 in their study of

133 Australian older patients (>65 years) in rehabilitation showed that malnourished

patients at admission were at higher risk (OR 2.3, 95% CI 1.1-4.8) of admission to

higher level of care upon discharge. Even after adjustment for confounding factors,

Covinsky et al94 showed that among 369 hospitalised elderly (>70 years) in US, those

who were malnourished at admission were more likely (OR 3.2, 95% CI 1.1-9.9) to

spend time in a nursing home within 1 year after discharge. However, the relationship

between malnutrition and discharge destination did not persist after adjustment for

covariates in a Spanish study conducted with 353 hospitalised elderly (mean age 82

years) 158.

1.6.4 Infections

Development of infections or sepsis during hospitalisation is a serious clinical

outcome. Studies have shown that malnourished adult patients are at up to two and a

half times higher risk of developing infections compared to well-nourished patients61,

89. Likewise among older adults, malnutrition was also reported to be associated with

development of sepsis in hospitalised patients in Europe81, 99, and an independent

predictor for development of in-hospital complications among more than 1000

geriatric rehabilitation patients in the US142, 143, 160, 161. However no standard

assessment methods of infections or complications were reported in these studies.


40

1.6.5 Hospital readmission rates

Hospital readmission was not as frequently studied as a healthcare utilisation outcome

among hospitalised older adults. Only three prospective studies116, 151, 152 were

identified and they consistently demonstrated poor nutritional status was predictive of

hospital readmission. Friedmann et al (1997)152 studied 92 geriatric patients (age

range: 65-92 years) in the US and showed that poor nutritional status was predictive

of non-elective hospital readmission within 3-months of discharge. Similarly,

Feldblum et al (2009)116 also reported that patients with MNA score <24 were at

higher risk of hospital readmissions at 3-months and longer readmission hospital stay

in their study of 204 elderly patients (>65 years) from an Israel hospital. Even after

adjustment for covariates among 110 elderly patients (>65 years) from a rehabilitation

hospital in the US, Sullivan (1992)151 was still able to demonstrate that malnutrition

(discharge serum albumin level not defined) was predictive of non-elective

readmissions at 3-months post discharge.

1.6.6 Functional status

Functional status is an important patient outcome which is usually defined by

measures of independence in activities of daily living (ADL). Common measures of

ADL include the Barthel Index162 and the Katz Index163 or self-report based on each

ADL function. Poor functional status on admission has been associated with

admission malnutrition in cross-sectional studies43, 44, 136, 164. However it is unclear if

poor functional status was a risk or consequence of malnutrition in these studies.

When protein and energy stores are depleted during an acute illness leading to

malnutrition, it decreases muscle strength and exacerbates the patient’s weakness,

contributing to a decline in the physical activities and functional status. This can


41

predispose the development of frailty and thus increase the risk of falls165. On the

other hand, when functional capacity decreases, it limits the physical ability to prepare

and consume adequate nutrition, and could increase the risk of poor nutrition41, 44.

Some nutrition assessment tools such as the MNA and SGA include measures of

functional status so it is possible that the two may be related44. Therefore to

demonstrate the predictive association of malnutrition with functional outcomes, the

study design must be a prospective follow-up one with the admission functional status

included as a covariate.

Only two prospective studies82, 94 demonstrated the predictive ability of malnutrition

on functional status among hospitalised elderly. Neumann et al (2005)82 showed in

their study amongst 133 elderly patients in rehabilitation (>65 years) that

malnourished patients (MNA score <24) on admission had significantly poorer

functional status (using Modified Barthel Index, mean+SD: 85+19 vs 96+7, p<0.05)

at 90 days, with adjustment for admission functional status. When Covinsky et al

(1999)94 adjusted for nine covariates in their study of 369 elderly patients (>70 years)

in the US, they also showed that malnutrition (by SGA) was predictive (OR 2.8, 95%

CI 1.5-5.5) of poor functional status (dependent in >1 self-reported ADL function) at

3-months post discharge.

Despite the widely reported associations between nutritional status and clinical

outcomes reviewed in this section, there is limited documentation on the impact of

malnutrition in the Singapore hospitalised patients60. In the Singapore study by Raja

et al60 (mean age 56 years), inpatient mortality was 15.5% and 1.3%, readmission rate

was 7.6% and 2.6%, and LOS was 7.9 days and 3.8 days, amongst the malnourished


42

and non-malnourished patients respectively (p<0.05, for all comparisons)60, after

adjustment for age and admission clinical disciplines. However, it did not show if

statistical differences between the clinical outcomes of the malnourished and non-

malnourished patients persisted in only the elderly patients. It is also not known if the

consequences of malnutrition in hospitalised older adults are more severe. Data on the

impact of malnutrition in hospital geriatric patients from Singapore are greatly

lacking.

Overall, malnutrition in older patients has been shown to be associated with adverse

clinical outcomes such as mortality, longer LOS, discharge to higher level care,

infections, hospital readmissions, and poorer functional status. Generally these

relationships have been less commonly studied in Asian elderly populations. Many of

these association studies did not adjust for confounding factors, or only selective

confounders were included. As a result, most of the evidence was not strong enough

to support the independent associations between malnutrition and poor clinical

outcomes.


43

1.7 Nutrition screening

With the high prevalence of malnutrition and the many adverse outcomes associated

with malnutrition, it could be argued for all patients to receive a detailed and

comprehensive nutrition assessment upon hospital admission by dietitians and have

their nutritional status monitored during admission (Figure 1-1). However this may

not be practical and cost-effective in clinical practice as it is time-consuming perform

a full nutrition assessment. Therefore nutrition screening is an important first step in

the early identification of malnutrition and initiates the whole nutrition care process

before comprehensive nutrition assessment is performed. It can potentially reduce the

high prevalence of undiagnosed and untreated malnutrition upon hospital admission.

The various nutritional screening methods are reviewed in this section and the

nutritional assessment approaches are discussed in the next section 1.8.

1.7.1 Importance and benefits of nutrition screenin g

Nutrition screening is a critical antecedent step in the Nutrition Care Process15. Its

purpose is to identify malnourished individuals or those at nutritional risk who would

benefit from more detailed nutrition assessment and individualised nutrition care.

Many national, international and specialist organisations such as British Dietetics

Association (BDA), European Society of Parenteral and Enteral Nutrition (ESPEN),

British Association of Parenteral and Enteral Nutrition (BAPEN), and Dietitians

Association of Australia (DAA) have recommended routine screening in various care

settings to identify patients at risk of malnutrition for further evaluation and treatment

112, 131, 166-168. A recent Cochrane review showed that malnourished older adults

benefited most from energy and protein supplementations169; hence it is crucial that


44

they are identified early for initiation of appropriate nutrition intervention. The

associated benefits of nutrition screening are shown in Table 1-12.

In Singapore, nutritional screening for all patients admitted to the hospitals is required

for accreditation by the Joint Commission International. In TTSH, a nutrition policy

for screening all patients within 24 hours of admission by the nursing staff using the

TTSH Nutrition Screening Tool (TTSH NST, Appendix Table A-4) has been

implemented since 2002.

Table 1-12: Benefits of nutrition screening 131, 170

1. Early identification and assessment of patients who need nutritional intervention i.e.

nutrition support 170, 171

• Improve clinical outcomes e.g. LOS 101, 140, 172

• Improve nutritional status 173-177

2. Reduce prevalence of malnutrition 135

3. Improve recognition and detection of malnutrition 178, 179

4. Increase nutrition-related and malnutrition documentation

• Better reimbursement with inclusion of malnutrition code in diagnosis-related groups

(DRG) 180

5. Increase referrals to dietitians131, 170

6. Reduce under-treatment of malnutrition; reduce patients missed for nutritional

intervention 170, 171, 181


45

1.7.2 Nutrition screening tools

There exist a variety of screening tools that incorporate different anthropometric,

biochemical and clinical criteria182, 183. A nutrition screening tool typically uses a

questionnaire-type format183 with each question examining a known risk factor for

malnutrition, and the summation of scores is used to categorise the nutritional risk183.

The tool is usually completed by nursing staff upon hospital admission184, by general

practitioner or healthcare professional in nursing homes185, or may be self-

administered in the community186. Depending on the score or risk category of the

nutrition screen, specified protocols for follow-up action or care plan (e.g. referral of

those screened at risk to a dietitian for more detailed assessment), would be

indicated187-191. Many of the screening tools developed for and applied in hospitals

include weight status (percent ideal body weight for height), weight change, diagnosis

relating to increased nutritional requirements, appetite or adequacy of dietary intake181,

187, 190-197. However, there are no standard criteria for nutrition screening tools.

Therefore the use of different tools may result in a diverse group of individuals being

identified as at risk of malnutrition198.

Nutrition screening tools can be evaluated by their predictive, criterion, and content

validity199, 200. Reviews of over 100 nutrition screening tools identified for general use

and for use in specific population group i.e. older adults182, 183, 201 showed several

limitations. The most common limitations are listed in Table 1-13. A key limitation is

that many screening tools have not been well- validated182, 183. There is no one best

screening tool available, however a good nutrition tool should include most of the

characteristics listed in Table 1-14. Most importantly, a screening tool should be

validated and easy to administer. Most screening tools developed for older adults are


46

for general use or more specifically for use in the community and institutionalised

settings201. There are limited screening tools developed specifically for hospitalised

elderly patients that are valid and easy to perform. As a result, general screening tools

developed for hospitalised adults are also applied to geriatric patients202, 203. However

their effectiveness and validity remain in question when administered on elderly

patients as there are limited validation studies in this population.


47

Table 1-13: Common limitations of available nutrition screening tools 182-184, 201

1. Lack of rigorous evaluation of their reliability and validity

2. Considerable differences between tools in validity, reliability, ease of use and

acceptability

3. Limited predictive ability 202

4. Various reference standards used for validation due to lack of “gold standard” for

malnutrition assessment method and diagnosis204

5. Use of true criterion to assess validity is questionable e.g. based only on pre-albumin or

mainly subjective 181, 191, 193, 205, 206

6. Validated only in specific population and setting, may not be applicable in other settings

due to environmental influences on various risk factors

7. Insufficient information to allow appropriate usage of tools

8. Lack of practical information on tool implementation

9. Non-objective screening parameters based on unsubstantiated criteria and cut-off points

10. Not suitable for hospital wide implementation by nursing staff

• Too lengthy and time-consuming 207

• Too complex or specialised, requiring dietitians to complete

• Invasive

• Nutrition parameters not routinely or easily available e.g. biochemical data 208, 209


48

Table 1-14: Characteristics of an effective nutrition screening tool 171, 183, 184, 199,

210-212

1) Simple, quick (<5min to complete)and easy to use and interpret

2) Reliable and reproducible

3) Construct, criterion and content validity

4) Sensitive, specific and predictive

5) Convenient to perform, easily completed by non-professional staff, patient or family

• Use routinely available data

• No laboratory data needed

• Involve no calculations

6) Inexpensive

7) Non-invasive

8) Acceptable to patients

Amongst the available and published nutrition screening tools, the Nutritional Risk

Screening 2002 (NRS 2002)166, Short Nutritional Assessment Questionnaire

(SNAQ©)179, Mini-Nutritional Assessment Short Form (MNA-SF)213, Malnutrition

Screening Tool (MST)184 and Malnutrition Universal Screening Tool (MUST)198

demonstrated characteristics of an effective nutrition screening tool, such as quick,

simple and validated. They are also recommended by DAA as valid screening tools in

acute care settings112. The Tan Tock Seng Hospital Nutrition Screening Tool (TTSH

NST) which was developed in TTSH among hospitalised adults has similar

components as the other established tools. Key characteristics of all these tools are

compared in Table 1-15. More details are described in sections 1.7.2.1-1.7.2.6 and the

tools are shown in Appendix Table A-3 to A-8.


49

Table 1-15: Comparison of characteristics of selected nutrition screening tools

MST184 NRS

2002166

MNA-

SF185 SNAQ©179 MUST198

TTSH

NST*

Country Australia Denmark US Netherlands UK Singapore

Population Adults Adults Elderly Adults Adults Adults

Setting Hospital Hospital All Hospital Hospital Hospital

Parameters:

Weight � � � � � �

Height � � � � � �

BMI � � � � � �

BMI cut-off

(kg/m2) - <20.5 <23 - <20 -

Weight loss � � � � � �

% weight loss � � � � � �

Physical

appearance � � � � � �

Dietary intake � � � � � �

Diagnosis/Disease � � � � � �

Feasibility:

No. of questions 3 4 6 3 3 4

Nursing staff � � � � � �

Training � � � � � �

Time required <3min <5min <5min <3min <5min <5min

Validation:

Hospital � � � � � �

Elderly � Limited � � Limited �

Singapore Limited � � � � �

Abbreviations: TTSH-NST: Tan Tock Seng Hospital Nutrition Screening Tool; NRS 2002:

Nutritional Risk Screening 2002; SNAQ©: Short Nutritional Assessment Questionnaire; MNA-

SF: Mini Nutritional Assessment Short-Form; MST: Malnutrition Screening Tool; MUST:

Malnutrition Universal Screening Tool.

*Refer to Appendix Table A-4 and Appendix TTSH Medical Board Paper on TTSH NST


50

1.7.2.1 Malnutrition Screening Tool (MST)

The Malnutrition Screening Tool (MST)184 (Appendix Table A-3) was identified in a

systematic review as a simple, quick, valid and reliable tool for identifying patients at

risk of malnutrition on admission to hospital204. It was developed by combining the

nutrition screening questions with the highest sensitivity and specificity at predicting

the SGA result scores among 408 medical/surgical hospitalised patients in Australia184.

Its sensitivity and specificity are both at 93%, indicating that it strongly predicts

nutritional status as defined by SGA184. The MST also presents a high inter-rater

reliability between dietitians and dietitian assistants, with kappa ranging between

0.84-0.93184. Although it was suggested that MST can be completed reliably by

nursing staff, the inter-reliability between dietitians/dietitian assistants and nursing

staff is yet to be established. The only disadvantage is that there was no blinding of

the reference standard during validation and the performance was evaluated in the

same population in which the tool was developed (lack of cross-validation).

1.7.2.2 Tan Tock Seng Hospital Nutritional Screenin g Tool (TTSH

NST)

When 658 medical and surgical patients in a Singapore hospital (TTSH) were

screened by dietitians using the MST, 22% were found to be at risk of malnutrition

and 15% were confirmed by SGA as malnourished (positive predictive value 68%) 60.

It appeared that the screening results from the MST were not as predictive of

malnutrition when applied to the Singapore acute hospital population compared to the

existing literature184. Subsequently, the MST was modified by including additional

questions and response options which were predictive of malnutrition (defined by

SGA), resulting in the development of the TTSH NST.


51

The TTSH NST (Appendix Table A-4) is simple and quick to perform. It is similar to

MST, except for additional questions on diagnosis and physical appearance. Clinical

physical appearance was found to have high levels of agreement with BMI in one

screening tool191. Hence the physical appearance question can be a useful substitute

for BMI which is often difficult to perform in the hospital setting as many patients are

unfit during acute admission. Moreover, no calculations are involved in using this tool.

Patients identified to be at risk (score >4) are referred to the dietitian for more detailed

nutrition assessment and appropriate intervention.

The inter-rater reliability of the TTSH NST between a dietitian assistant, nurse and

dietitian ranged from 94 -97% when completed on 34 patients. Validity was evaluated

in 121 general medical and surgical patients (mean age and range not reported) who

were screened by nurses within 24-48 hours of admission. The sensitivity, specificity,

positive predictive value, negative predictive value and accuracy were 100%, 99%,

93%, 100% and 99%, respectively, when compared against the SGA assessed by the

dietitians (Appendix: TTSH Medical Board Paper on TTSH NST). I was involved in

demonstrating the internal validity of the TTSH NST and the tool has implemented

hospital-wide since 2002 as the standard nutrition screening form used by the nurses

within 24 hours of admission. However, its usefulness and validity in the hospitalised

elderly patients have not been evaluated.

1.7.2.3 Nutritional Risk Screening 2002 (NRS 2002)

The NRS 2002166 (Appendix Table A-5) was developed for patients in acute-care

hospitals based on the results of 128 randomised controlled trials showing the specific


52

population to whom nutrition intervention was beneficial. The European Society for

Parenteral and Enteral Nutrition (ESPEN) recommends the NRS 2002 tool to screen

malnutrition and to assess the risk of developing malnutrition in hospital settings 200.

The first section of the NRS 2002 consists of four initial screening questions

(Appendix Table A-5a). A positive response to any of the screening questions triggers

the full test to be completed166. Patients are characterised in the subsequent final

screening (Appendix Table A-5b) by scoring the components “impaired nutritional

status” and “severity of disease”. “Impaired nutritional status” is evaluated using three

variables (BMI, per cent weight loss and recent change in food intake). The most

compromised of the three variables is used to categorise the patient. Although BMI is

included as one of the components, it is not essential to calculate BMI as changes in

weight or dietary intake can be rated instead.

The NRS 2002 has a high predictive validity214-216. When applied prospectively in a

controlled trial with 212 hospitalised patients selected according to this screening

method, a reduced LOS (14+2 days vs 20+2 days, p<0.05) among patients with

complications in the intervention group was shown214. When compared to the MNA

and MST, nutritional risk assessed by the NRS 2002 was the only screening tool

shown to be an independent risk factor for longer LOS among 207 hospitalised

elderly patients (mean age 74+7 years) in Portugal215. In addition, the NRS 2002 was

also shown in a sample of 705 Brazilian hospitalised patients (mean age 57+15 years)

to be predictive of severe complications and death 216.


53

The NRS 2002 is quick and easy to perform as it does not require physical

examination or anthropometric measurements and calculations. The tool also showed

good reliability with low inter-observer variation (k=0.67) between a nurse, dietitian

and physician54. Its high practicability is evident where 98-99% of all newly admitted

younger and older patients could be screened54, 203 and has been mainly applied in

Danish hospitals54, 217. With the ease of administration and high completion rate, the

NRS 2002 is a practical and useful screening tool for hospitalised patients. However

the NRS 2002 was not developed specifically for an elderly population, although it

includes an additional scoring for age >70 years. Currently there are limited

validations done in the elderly203, 206, 215.

1.7.2.4 Mini Nutritional Assessment Short Form (MNA –SF)

The MNA is both a screening and assessment tool developed for the elderly

population that has been widely used in different settings and countries218. However,

the full MNA is too complex and lengthy to fulfill the characteristics of a good

screening tool213 (Table 1-19). The MNA-SF185 is a validated shortened version of the

MNA (Appendix Table A-6) which is reliable, quick and easy to use for screening185.

When combined with the MNA, the MNA-SF can be used in a two-step process213.

The MNA-SF (step one) consists of a subset of six questions from the full MNA

which correlate best with MNA total score. The maximum screening score is 14. A

MNA-SF score <11 suggests risk for malnutrition and confirmation is done by

proceeding to complete the full MNA (step two, assessment).

The MNA-SF is an efficient and reliable tool that can be easily administered by

healthcare professionals in both community and acute settings to identify risks of


54

malnutrition185, 218. It has high sensitivity and specificity of >95% when compared to

the full MNA213. The MNA-SF had been applied in two small studies among acutely

ill older adults in Sweden219 (n=40, mean age 84 years) and Norway220 (n=69, mean

age 82 years). All patients were identified as at risk of malnutrition in the former

study in Sweden219, however no additional validation of the tool was reported. In the

latter Norwegian study, the MNA-SF demonstrated high sensitivity (100%) when

compared to the clinical assessment by the nutritionist220. However, false positive

rates (low specificity) were also high as it identified both malnourished patients as

well as those at risk. Since the MNA-SF is a subset of the full MNA, its performance

is expected to be good when compared against the latter. From the literature reviewed,

the MNA-SF has not been validated against another reference standard to determine if

the reported good performance remained.

A limitation of the full MNA is that it is not suitable for patients who are cognitively

impaired or cannot provide a reliable self-assessment, such as those with confusion,

advanced dementia, serious post-stroke aphasia or apraxia, and sometimes even those

with severe acute diseases like pneumonia. Hence many patients cannot be assessed

using the MNA in acute settings203 and it may be a more suitable tool for outpatient

settings. However, the MNA-SF includes questions which need not be answered by

patients and this may allow a higher completion rate. It has recently been updated and

validated to include calf circumference as the alternative screening question when

BMI is not obtainable 221, 222

Although widely reported to be useful among Caucasians, the MNA-SF may not be

applicable to ethnic groups with non-western cultural, dietary habits or health care


55

systems, nor to different population means for statures and weight223. There are

limited studies to establish its usefulness and validity among non-Caucasian

populations. A Chinese Nutrition Screen (CNS) was developed and validated for

institutionalized older adults based on the MNA and included questions tailored to

suit the Chinese health care system, diet, food customs and culture. It consisted of 16

questions and the BMI measurement is excluded223. Although it is potentially useful

for application in Singapore due to some ethnic and cultural similarities, it may be too

lengthy and time consuming. With 16 questions to be completed by the nursing staff,

it will not be deemed a suitable screening tool in an acute care setting.

1.7.2.5 Short Nutritional Assessment Questionnaire (SNAQ ©)

The SNAQ© (Appendix Table A-7 ) was developed based on the results of nutritional

status and characteristics of 291 patients (58.4+18.3 years) admitted to a mixed

internal medicine ward and a mixed surgical ward (general surgery and surgical

oncology) in the Netherlands179. It was developed in accordance with the

requirements and guidelines for nutritional screening tools by ESPEN200. The three

questions relating to weight loss, decreased appetite, and use of supplemental drinks

(Appendix Table A-7) were included in the SNAQ©. These items can be easily scored

by the nurse at admission as no measurements or calculations are required. Like the

MST and TTSH NST, the SNAQ© is quick and simple to use and has been identified

in a systematic review as a valid screening tool in the hospital setting204. However,

unlike the former tools, the SNAQ© does not include the response option of “unsure”

in the weight loss question, which could also potentially indicate malnutrition risk.

The tool classifies patients as moderately malnourished (score >2) or severely

malnourished (score >3). A treatment plan is developed depending on the total score,


56

where a dietitian’s intervention with energy- and protein-enriched meals is indicated

for severely malnourished patients.

The sensitivity and specificity of the SNAQ© were >75% when tested against the

same assessment method of malnutrition (BMI <18.5kg/m2 and unintentional weight

loss of at least 5% in the last 6 months) in another population of 297 patients170. The

inter-rater reliability was 0.69 and 0.91 (kappa) between the nurse/nurse and

nurse/dietitian, respectively. The tool has not been demonstrated to predict clinical

outcomes as shown by the NRS 2002. Although developed for the purpose of

screening patients at risk of malnutrition, the total score classifies the patient’s

nutritional status (moderately or severely malnourished). A dietitian’s consultation is

required only when a patient is classified as severely malnourished (score >3)170.

Therefore if it is applied strictly as a screening tool, patients should be considered at

risk when they have a score of >2.

1.7.2.6 Malnutrition Universal Screening Tool (MUST )

The MUST (Appendix Table A-8) was developed by the Malnutrition Advisory

Group of the British Association for Parenteral and Enteral Nutrition (BAPEN) in

2003198. The tool was initially developed for use in the community and comprises of

three criteria: current weight status using BMI, unintentional weight loss over time

and an acute disease parameter for those expected to have a significantly diminished

food intake for more than five days198. It is easy and quick to perform, except that

patients must be weighed for the calculation of BMI and weight loss. For those who

could not be weighed or have their height measured, their malnutrition risk was

established using recalled or documented values, surrogate measures and subjective


57

criteria. The MUST has been criticised by a nursing group in the UK as being too

difficult to complete because of the BMI calculation211. This is widely considered the

main limitation of the tool.

The MUST has been documented to have a high degree of reliability between

healthcare providers (k=0.88-1.00). It has been widely implemented in the UK across

all settings and its practicability, concurrent validity and predictive validity for LOS,

mortality and discharge destination have also been demonstrated198, 202. Although the

MUST was not developed specifically for the elderly population, it was recently

evaluated with 150 elderly hospitalised patients (mean age 85+5.5 years)202. Weight

could only be measured in 56% of patients and height was measured in only 16% of

patients. In most cases, these measurements were obtained from reliable recall, recent

documentation or surrogate measures. The MUST risk category was shown to be

predictive of in-hospital, 3 and 6 months mortality and LOS. The relationship between

MUST scores and poorer outcome persisted when only recalled or subjective data was

used in the MUST risk categorisation. The MUST was not as sensitive and specific as

the NRS 2002 in identifying malnutrition risk when compared against the SGA in 995

hospitalised adult patients in Switzerland 224. It was also not as predictive of clinical

outcomes such as LOS, as the NRS 2002224.

Overall, the choice of an effective nutrition screening tool is important to accurately

identify patients at risk of malnutrition. The MST, TTSH NST, NRS 2002, MNA-SF,

SNAQ© and MUST have been identified as valid tools in the acute care setting.

However many of these tools were validated in adult Caucasian populations and few

have been evaluated specifically with older adults. In addition, the use of different


58

reference standards for nutritional status in the validation studies makes comparisons

between tools more challenging.


59

1.8 Nutrition assessment

The aims of nutritional assessment are to identify patients who have protein-energy

malnutrition, to quantify a patient’s risk of developing malnutrition–related medical

complications and to monitor the adequacy of nutritional therapy225. There are

numerous parameters such as anthropometric, biochemical, clinical, dietary,

functional, immunological, and multi-parameter tools used in nutritional assessments.

These parameters are listed in Table 1-16. Some of these are more commonly used as

assessment methods of malnutrition (previously shown in Table 1-4) in the

determination of nutritional status, and are discussed in more detail in sections 1.8.1

to 1.8.6.


60

Table 1-16: List of parameters and indices used in nutrition assessment

Anthropometric • Height

o Knee height o Demi-span o Arm span

• Weight o Usual weight o Ideal body weight o Weight-for-height ratio o Body mass index (BMI)

Absolute weight loss o Percentage weight loss o Percentage ideal body weight

Estimates of fat and muscle tissues • Triceps skinfold thickness (TSF) • Mid-upper arm circumference

(MAC) • Mid-upper arm muscle

circumference (MAMC) • Mid-upper arm muscle area (AMA) • Corrected mid-upper arm muscle

area (CAMA)

Biochemical • Plasma proteins

o Serum albumin o Serum pre-albumin o Serum total protein o Serum transferrin o Retinol binding protein o C-reactive protein

• Iron status indices o Total iron-binding capacity

Haemoglobin o Hematocrit

• Urinary indices o 24-hour urinary urea

nitrogen o 24-hour urinary creatinine o Creatinine height index o Nitrogen balance

• Vitamins and minerals

Clinical • Medical history • Physical examination

Dietary • Diet history • 24-hour diet recall • Diet records • Food frequency questionnaire

Functional • Hand-grip dynamometry • Respiratory function tests • Walking tests

Immunological • Total lymphocyte count (TLC) • Skin test reactivity -Delayed cutaneous hypersensitivity (DCH)

Multi-parameter tools • Subjective Global Assessment (SGA) • Mini Nutrition Assessment (MNA) • Prognostic Nutrition Index (PNI) • Nutritional Risk Index (NRI)


61

1.8.1 Anthropometry

Anthropometric measurements are simple, inexpensive, and non-invasive bedside

techniques that provide an indirect estimation of body composition. They are the most

commonly used methods to measure body size and composition, and nutritional status

in older adults in clinical practice226 compared to other more accurate indirect

methods, such as dual energy X-ray absorptiometry (DEXA) which is only possible in

research settings227.

Height, weight, waist and hip girth, skinfold thickness and arm muscle circumferences

are the most common anthropometric measures used in clinical practice228, 229. The

physical state of patients may limit their ability to assume the appropriate positions

for measurements to be taken. However, most measures can be taken whether the

patients are in an upright (seated or standing) or supine (recumbent) position230, 231

and are equally accurate and reliable231, 232. This flexibility of the measuring positions

would not exclude many of the hospitalised elderly who may be disabled or bed-

bound, from these anthropometric measurements. Sections 1.8.1.1 to 1.8.1.7 discuss

their clinical applications.

1.8.1.1 Weight

Weight is a measure of body mass and is integral to anthropometric measurements. It

is required in the calculation of body mass index (BMI: weight in kg/height in m2)

which is a measure of body size rather than a measure of body composition. Weight is

commonly used as a baseline measurement for monitoring nutritional status during

hospitalisation59, 69, 137, and recommended by the DAA in the malnutrition practice

guideline as an important nutrition outcome112. Weight should be obtained on a


62

calibrated scale with attention to dehydration, oedema, ascites and amputation.

Weight measurement in non-ambulatory persons can be difficult and may require

special equipment such as chair scales. For those who are extremely ill or bedbound,

weight measurement can be extremely challenging and even impossible if bed scales

are not available.

1.8.1.2 Weight loss

Weight history over a period of time is more valuable clinically than a single weight

measurement233, 234. Fractional change in weight accounts for the variability in

baseline weight and is the most clinically relevant measure233. Unintentional weight

loss of >5% in 30 days or >10% over 6 months have been considered clinically

significant26, 235, 236, and is associated with adverse clinical outcomes233, 237. Old age,

low baseline body weight, disability, and coexisting medical illnesses have been

associated with increased risk of weight loss238-240. Unintentional weight loss of at

least 5%, regardless of baseline weight, was shown to be an independent risk factor of

mortality229, 239 and functional disability241 among community-dwelling older adults.

Among hospitalised patients, unintentional weight loss of >10% over 3 months prior

to hospital admission was also shown to be predictive of hospital mortality242. This

association was also evident in non-Caucasian countries where unintentional weight

loss (>5 kg over 3 years) among free-living older Chinese in Hong Kong was

associated with increased mortality and morbidity243.

Weight history prior to hospitalisation is a valuable assessment component in the

elderly as it is independent of normative reference values. It has been frequently used

with other anthropometric measures such as BMI, for diagnosis of malnutrition59, 65,


63

134. However, absolute amount of weight loss is difficult to determine when a baseline

weight is not available. Based on personal clinical experiences, many older adults are

unable to recall their habitual (baseline) weight or seldom have their weight

measured; hence its application may be limited. Weight loss assessment history based

on patient recall may be subject to error (up to +7.2 kg) compared to observed weight

244. In situations where weight losses are unknown, or baseline weights are not

available, or the amount of weight lost is unquantifiable, attempts to establish weight

change could be made through subjective questioning e.g. if clothes have become

more loose fitting. However, subjective measures are less reliable than objective

measures. The DAA malnutrition practice guidelines recommends the measure of

body weight upon hospital admission and at regular intervals during admission e.g.

weekly, to monitor any weight changes during hospitalisation and between

admissions112.

1.8.1.3 Height and proxy measures

Body mass index, a common nutrition assessment parameter, is dependent on the

accurate measurements of height. However, accurate measure of standing height in

the elderly poses problems due to the loss of height associated with age-related

shortening of the spinal column (disc space narrowing), kyphosis and spinal

osteoporosis245, 246. Patients may also be too ill to stand straight for height to be

measured or have lower extremity amputation. It was possible to measure height

reliably in only 30% of the hospitalised patients in acute geriatric wards in Hong

Kong247. Satisfactory alternatives are demi-span248, arm span247or knee height249, 250.

These are long bones that do not decrease significantly with age in the same way as

the spine246, 251. These measurements do not generally require a trained observer are


64

reproducible and demonstrate high correlation coefficients with measured standing

height247, 249.

Predictive equations are used to estimate height from these alternative measures. Knee

height is the most commonly used height estimates among older adults, and has better

inter-observer reliability than arm lengths249. Some of these equations were derived

from Caucasian or non-elderly populations as shown in Table 1-17. It is important

that predictive equations chosen for application should be derived from a population

with similar characteristics as the study population. There are three predictive

equations for knee height which were developed in the Asian populations252-254 (Table

1-17), but only one was derived from an elderly population253.


65

Table 1-17: Predictive equations for height using knee height

Non-Hispanic

White Men 78.31 + (1.94 x knee height)– (0.14 x age)

White Women

Non-Hispanic

82.21 + (1.85 x knee height)– (0.21 x age)

Black Men 79.69 + (1.85 x knee height)– (0.14 x age)

Chumlea et al, 1998255:

Based on 4,750 elderly aged >60

(1,369 non-Hispanic white men,

1,472 non-Hispanic white women,

474 non-Hispanic black men, 481

non-Hispanic black women, 497

Mexican-American men, 457

Mexican-American women)

Black Women

Mexican-

American

Men

Women




Donini et al, 2000256:

Based on 84 Italian aged >60 years

Men

Women

94.87 +(1.58xknee height)-(0.23 x age) +4.8

94.87 +(1.58xknee height)-(0.23 x age)

Li et al, 2000253:

Based on 164 Chinese women and

89 Chinese men >60 years in Hong

Kong

Men

Women

51.16 + (2.24 x knee height)

46.11 + (2.46 x knee height) – (0.12 x age)

Cheng et al, 2001252:

Based on 1179 Taiwanese (aged

25-85 years)

Men

Women

85.1 +(1.73 x knee height) – (0.11 x age)


Mendoza-Nunez et al, 2002257:

Based on 736 Mexican (men 186;

women 550) aged 60-97 years

Men

Women

52.6 +(21.7 x knee height)


Shahar & Pooy, 2003254:

Based on 100 Malaysians

(42.3+5.8 years, 52% Malays,

38.5% Chinese)

Men Women 69.38 + (1.92 x knee height)

50.25 + (2.23 x knee height)


66

1.8.1.4 Body Mass Index (BMI)

BMI is an index of weight-for-height that is commonly used to classify overweight

and obesity in adults. Reference ranges have been defined according to the extent of

adiposity and the level of morbidity risk. The WHO classifies normal weight as 18.5-

24.9 kg/m2 and BMI <18.5 kg/m2 is considered underweight, reflecting chronic

energy deficiency and indicative of malnutrition258-260.

BMI may reflect different levels of body fatness in different age and ethnic groups261,

262. Singaporeans have higher percentage of body fat at low levels of BMI263, and

experience elevated levels of cardiovascular risks at relatively lower BMI263. It is

strongly suggested to lower the BMI cut-off values for overweight and obesity among

Singaporeans, from the WHO values of 25 kg/m2 and 30 kg/m2 to 23 kg/m2 and 27

kg/m2, respectively264. Likewise, a different cut-off value may be applicable to the

assessment method of underweight and malnutrition among non-Caucasian

populations and Singaporeans.

There is a lack of consensus on the appropriate BMI cut-offs for elderly. Although

BMI has traditionally been used as the predictor of mortality in younger adults,

studies investigating the relationship between lower BMI (cut-offs <20 kg/m2 & <22

kg/m2) and mortality in hospitalised older adults have shown inconsistent results146,

229, 242, 265, 266. When higher BMI cut-off values (<26 kg/m2) were applied81, lower

BMI values were associated with higher mortality rates in older adults267. A review by

Beck and Ovesen268 indicated that the optimal range of BMI for older adults is higher

at 24-29 kg/m2 based on its prognostic significance. However in another review, the

use of age-specific BMI cut-offs was not recommended as much of the data did not


67

consider covariates i.e. pre-existing diseases in the elderly107. Studies conducted

among older Asians showed that the acceptable range for BMI was lower at 18.5-23.0

kg/m2 264, 269. It has been shown that BMI <18.5 kg/m2 was predictive of mortality

among 154 nursing home residents (mean age+SD:77+12 years) in Singapore78. The

application of higher BMI cut-offs among Asian elderly populations is not known. If

higher BMI cut-offs are applied to Asian or Chinese elderly populations, a large

proportion of elderly would be considered at risk or malnourished73

Challenges and difficulties in obtaining height and weight for hospitalised older adults

limit the application of BMI in clinical practice. In addition, changes in body

composition associated with ageing and the related ethnic differences make the

clinical application of BMI in older adults and the selection of appropriate cut-offs

difficult in a Singapore context. Nevertheless, BMI remains a common and widely

used nutrition index in the assessment method of malnutrition among older adults

across all care settings157, 242, 266, 270-272.

1.8.1.5 Triceps Skinfold Thickness (TSF)

Skinfold thickness provides a cheap and non-invasive assessment of subcutaneous fat

and represents a measure of total body fat273. The technique is reliable in trained

assessors and generally well-tolerated by patients273. For best accuracy, fat thickness

should be measured from multiple areas of the body. However, if a single area were to

be chosen, it should be the triceps, which was found to be the most accurate when

various sites were compared with hydrostatic weighing274. It is the site most

frequently selected for single, indirect estimate of body fat228. TSF is age, gender and

population specific, so age and gender standardised percentile cut offs may be used as


68

an indicator of body fat depletion275. For example <10th percentile (i.e. <8.1mm for

men 60-69 years based on white subjects from the US) is an indicator of poor

nutritional status276. TSF was shown to be an independent predictor of mortality

among older adults in the community277, 278 and in the hospital279, though not

consistently280. It is also used as one of the common nutrition indices for monitoring

change in nutritional status during admission59, 101. However, TSF may be uncommon

as a sole criterion for defining malnutrition in hospitalised adults230, 276.

1.8.1.6 Mid-arm circumference (MAC)

Arm circumference is an easy and non-invasive measurement. It provides an indirect

assessment of muscle mass, and may serve as an index of the protein reserves in the

body281. MAC is a feasible measurement and can be performed in acutely ill

patients245. MAC measurements are also influenced by age and gender, therefore age

and gender appropriate percentile cut-offs are used to define the level of body protein

depletion. For example, MAC<10th percentile (i.e. <28.6cm for men 60-69years in

France) has been used to indicate muscle depletion123. MAC was shown to be a

significant predictor of mortality among hospitalised patients242, 279 and long term care

older adults282. However, the validation study of MAC against the SGA on 158

hospitalised medical patients in the UK by Burden et al (2005)230, failed to

demonstrate its validity and prognostic value. Although MAC could be reliably

measured, it may not be suitable as a stand-alone measure to define malnutrition in

hospitalised patients230. However, MAC could be a useful nutrition index to monitor

nutritional status during hospitalisation59, 123.


69

1.8.1.7 Derivative arm indicators

MAC and TSF measurements are used to derive the mid arm muscle circumference

(MAMC), mid arm muscle area (MAMA) and corrected arm muscle area (CAMA),

which serve as more sensitive indicators of somatic protein reserves283. The equations

used to derive these indicators are shown in Table 1-18. MAMC, a one dimensional

measure, was shown to be a poor predictor of malnutrition in any age group284 as it

undergoes a proportionately smaller change than MAMA when the volume of upper

arm muscle declines in malnutrition284. However, MAMC may be useful in

monitoring change in nutritional status101, 139, and was shown to be associated with

functional status and mortality among older adults in the community285. MAMA, a

two-dimensional measure, was shown to be an independent predictor of mortality

among older adults in the community278. It is preferable to MAMC as an index of total

body muscle mass as it more accurately reflects the changes in muscle tissue276. But it

is rarely used alone as a nutrition index to define malnutrition among hospital older

adults, and tends to underestimate the extent of muscle atrophy281.

CAMA represents the absolute amount of mid arm muscle, excluding bone mass and

other non-muscle tissue. Like the MAMC and MAMA, it can be obtained in almost

all people and is not significantly altered by fluid status and conditions such as

dehydration and heart failure286. The predictive equations for CAMA have not been

validated specifically for use with older adults281. However, it has been shown to be

an useful nutrition index for defining malnutrition in elderly subjects and is more

sensitive than MAMA in detecting changes during muscle atrophy286. CAMA (<

16cm2) was shown to be superior to hypoalbuminaemia in predicting mortality280 and

could predict mortality up to 46 months (CAMA<5th percentile; OR 2.8, 95% CI 1.2-


70

6.5) in 758 community elderly aged >70 years in New Zealand277. In the Australian

Longitudinal Study of Ageing, CAMA (men:<21.4cm2; women:<21.6cm2) was shown

to be superior to BMI in predicting mortality up to eight years among 1396 elderly in

the community229, 287. This prognostic value of CAMA(<5th percentile) has also been

demonstrated among 219 hospitalised elderly patients in Switzerland79. The use of

CAMA as a malnutrition assessment method among older adults is promising. Studies

on the application of CAMA among older Chinese are limited288 and the predictive

ability of CAMA in non-Caucasians has yet to be evaluated.

Table 1-18: Equations for derivative arm indicators

Mid-arm muscle circumference (MAMC)

(Gurney & Jelliffe, 1973)283

MAC – (π x TSF*)

Mid-arm muscle area (MAMA)

(Gurney & Jelliffe, 1973)283

[MAC - (π x TSF)]2/ 4 π

[MAC - (π x TSF)]2/ 4 π – 10 (Men) Corrected arm muscle area (CAMA)

(Heymsfield et al, 1982)286 [MAC - (π x TSF)]2/ 4 π – 6.5 (Women)

*TSF: triceps skinfold thickness

1.8.1.8 Issues with anthropometric measures

Validity

Many changes associated with ageing influence the application and interpretation of

anthropometric measures. For example, changes in body composition, stature, weight,

tissue elasticity and compressibility that accompany ageing95, 289, 290 influence

accuracy. The apparent redistribution of fat from subcutaneous to deep adipose

tissues, decreased elasticity of skin, changes in hydration, alterations in skin

thickness, poor tissue separation for skinfolds, and atrophy of subcutaneous

adipocytes contributing to increased tissue compression226, 291 may cause the TSF


71

measure and the related derived indicators for muscle areas (MAMA, CAMA) to be

less accurate and valid in the elderly227, 292 and likely under-estimating the per cent

body fat. Problems relating to the measurement of weight and height have been

previously discussed in sections 1.8.1.1 and 1.8.1.3, respectively.

Interpretation

A key issue in the interpretation of anthropometry measures is the lack of age and

population specific reference standards, cut-off values and regression equations (for

knee height) which influence the predictive capability of the measurements.

Older adults generally experience a decline in anthropometric measures (BMI, TSF,

MAC, MAMC and MAMA) with age57, 293. Most of the reference data available are

inappropriate, with unrepresentative sample of a younger population276, 294. There are

limited anthropometric reference data for elderly >80 years289, 291, 293. Moreover,

differences in normative standards between populations have been observed276, 293, 295

due to geographic and ethnic variations in anthropometry measurements reflecting

lifestyle, environmental and genetic differences296. Most of the available data are from

the US and UK225, 276, 293, 295, 297, with few normative data for elderly in Asian

countries298 and none in Singapore. The need to develop population specific reference

standards250 is highly recommended by the WHO Expert Committee as the

applicability of the current available data as a standard to other populations is not the

most appropriate299.

Age and ethnic differences in anthropometric measures also influence the accuracy of

the predictive equations i.e. calculating height from knee height in the elderly248, 300,


72

301. Many of the original equations were developed based on younger population;

hence the predicted height approximated the maximum stature in adult. As a result,

some age and population specific regressions equations were developed to improve

the accuracy of these estimations in the elderly to reflect their current height (Table 1-

18)253, 254, 302, 303. Despite the availability of the different equations, there are no

recommendations from the literature regarding the choice of height proxies in the

BMI calculations of the elderly259. It is also unclear how well these available

equations could be applied to the older Singaporeans.

The use of appropriate reference standards has significant impact on the interpretation

of the measurements and assessments of malnutrition in the elderly. Therefore,

accurate nutritional assessment of the older adult patients would require the

development of norms specific to the population. It is important that age specific

standards for older adults are available as they should not be extrapolated from data of

people in their early sixties299.

1.8.2 Biochemical

Biochemical tests are more sensitive and objective than methods such as

anthropometry in detecting recent changes in nutritional status. However, most of the

tests are not available routinely in clinical practice, are expensive (to both patient and

institution), are altered by ageing and may be difficult to interpret in older people290.

Moreover, it is often difficult to separate the effects of actual nutritional deficits from

those of disease process in altered measurements seen among acutely ill patients.

Hence there are limitations in using laboratory data alone.


73

1.8.2.1 Serum albumin

Albumin is one of the most extensively studied and easily available serum proteins.

The use of albumin as a tool to predict morbidity and mortality had been widely

reported24, 62, 279, 304. Serum albumin was found to be one of the best single predictors

of morbidity and mortality among the elderly280, 284. However its usefulness in the

diagnosis of malnutrition in the elderly is disputed by Friedman et al280. Studies which

showed serum albumin to have prognostic significance in hospitalised elderly62, 279 did

not control for the effect of non-nutritional confounding variables such as illness

severity, on clinical outcome305. Hence low levels of serum albumin were more likely

to indicate severity of illness as a predictor of risk, rather than identifying protein-

energy malnutrition.

Despite the prognostic strength for mortality and morbidity, there are several

limitations associated with the use of serum albumin as a sensitive and specific

assessment method for malnutrition in hospitalised elderly. With the long half-life of

serum albumin (approximately 20 days), delayed responses to nutritional depletion

could occur to prevent early detection of malnutrition225, 306. Chronic alteration in

albumin could occur with diseases involving the hepatic system such as hepatic

failure. In the hospitalised patients, dehydration, posture, acute inflammation and

renal diseases would have influences on the albumin levels. Serum albumin was also

shown to be poorly correlated with other measures of nutritional status i.e.

anthropometry and SGA in hospitalised elderly306, 307.


74

1.8.2.2 Total Lymphocyte count (TLC)

TLC reflects the immunity status of patients. It is easily available and appropriate for

all age groups284. It has been shown that malnutrition leads to impaired immunity in

elderly patients62, 308. TLC has been proposed as a useful indicator of nutritional status

and outcome, with low TLC being associated with increased mortality208. An absolute

lymphocyte count less than 1500 per mm3 indicates malnutrition if other causes of

lymphopenia such as bacterial infections, can be excluded. However, TLC decreases

in cases of stress, tumours, sepsis and steroids, independent of malnutrition. In

addition, reduced TLC has also been shown to be associated with old age62, 309, 310

instead of nutritional status. Therefore the use of TLC as an indicator of malnutrition

among hospitalised elderly needs further evaluation as there have been limited studies

which used TLC alone for malnutrition assessment method63.

1.8.3 Dietary

Dietary assessment methods have many limitations in their applications especially

among hospitalised elderly. For acutely ill and cognitively impaired patients,

obtaining information on usual intake through recall can be difficult and time-

consuming311, with limitations in its accuracy and compliance312. Evidence suggests

that unbiased retrospective estimates of diets (diet history) are unobtainable and

increased age is found to be associated with decreased recall ability, affecting the

reliability of the assessment108, 313. Although dietary records have the advantage of

being less dependent on memory314, numeric and literacy skills are required for

estimating portions sizes and recording food consumed. Many of the elderly may not

be equipped with these skills to record their intake.


75

1.8.4 Clinical

Physical examination of clinical signs suggestive of malnutrition such as thin, sparse

hair, lesions of the lips, tongue and skin, can provide a useful guide, but such

relatively unspecific signs cannot be considered to indicate malnutrition without

supporting dietary and biochemical evidence114.

1.8.5 Multi-parameter nutrition assessment tools

While changes in fat and muscle stores may only occur after a prolonged period of

nutritional inadequacy, it is less sensitive to diagnose malnutrition based on any one

of these changes alone315, 316. Multi-parameter nutrition assessment tools have higher

sensitivity and specificity than single parameters in predicting nutritional status and

poorer clinical outcomes112, 317. Therefore, multi-parameter tools have been developed

to include more dimensions i.e. socio-psychological, medical and functional factors to

the assessment of malnutrition instead of focusing mainly a single factor. It has been

shown that nutritional assessment tools i.e. the Subjective Global Assessment (SGA)

and Mini-Nutritional Assessment (MNA), are more predictive of adverse clinical

outcomes among hospitalised patients than any single parameters95, 113, 144, 230, 318-324.

1.8.5.1 Subjective Global Assessment (SGA)

SGA (Appendix Table A-1) is an example of a nutrition assessment tool which

includes the multi-dimensional nature of assessing nutritional status325. It provides a

systematic way for evaluating nutritional status using various criteria, for example

patient history i.e. weight history, dietary intake, gastrointestinal symptoms,

functional capacity, metabolic demands of diseases and physical examination. The

characteristics of the tool are summarised in Table 1-19. The final assignment to the


76

category of nutrition status requires a certain level of professional and clinical

judgment as there is no numerical scoring to achieve a final SGA rating. A guide was

provided for the subjective assessment to place most of the judgement on the

variables weight loss, poor dietary intake, loss of subcutaneous tissue, and muscle

wasting235.

The SGA has been recommended by the ASPEN as the best tool to assess nutritional

status326, by ESPEN as an acceptable assessment tool12, and by DAA as a suitable tool

for use in acute care setting112. It has been used in a wide variety of clinical patient

groups, including surgical327, renal318, 328, 329, HIV330, 331, oncology332 and geriatric95,

113, 129, 203, 333, and is often regarded as a “gold standard’ method against which new

nutritional screening and assessment methods are validated334, 335. An advantage of

using the SGA is that biochemical tests are not required, making it a useful and non-

invasive nutrition assessment tool. Moreover, the SGA has been shown to be a

reliable and valid nutritional assessment tool in various patient populations336, 337. It

has good inter-rater reliability (kappa >0.70)235, 320, 338 even when performed by

inexperienced professionals (kappa 0.66)321, however, agreement was higher between

trained assessors (85%; kappa 0.75)95 compared to inexperienced assessors (79%;

kappa 0.66)321. The SGA was also shown to be predictive of clinical outcomes i.e.

mortality and complications327, 334 making it a good prognostic tool.

Although the SGA was not developed specifically for the elderly, studies have been

conducted to demonstrate its validity, reliability and predictive value for use in this

population group. The SGA was shown to be a valid nutritional assessment technique

in two studies among 90 hospitalised elderly patients (mean age 83 years) and 261


77

elderly residents (>65 years) in municipal care from Sweden. In both studies, the SGA

was validated against a combination of nutritional indices that included two or more

subnormal anthropometric (weight index, TSF, MAMC) and biochemical indices

(ALB, transthyretin)93, 129. However, these indices have their own limitations in

defining malnutrition (discussed in the earlier sections) and may not be the most

appropriate criteria for validation.

The SGA was shown as a good prognostic tool in hospitalised older patients in the

US, Sweden and Canada, with it being predictive of mortality up to one year

following discharge94, 113, 333. Hospitalised older patients (n=369, >70 years) in the US

classified as severely malnourished by the SGA had higher mortality rates within 12

months of discharge (OR 2.8, 95% CI 1.5-5.5), more likely to experience ADL

dependency at 3-months (OR 2.8, 95% CI 1.1-7.5), and be discharged to a nursing

home (OR 3.2, 95% CI 1.1-9.9)94. In Sweden, a study by Persson et al showed that the

SGA was predictive of mortality at one year among 88 hospitalised geriatric patients

(mean age 83 years)113. Duerksen et al (2000)333 also found increased mortality at 6-

months to be associated with an SGA classification of ‘severely malnourished’ in 95

consecutive patients (>70 years) admitted to geriatric and rehabilitation units in

Canada. However, there are no studies to date to demonstrate the application and

usefulness of the SGA in elderly Asian populations.

1.8.5.2 Mini Nutritional Assessment (MNA)

The MNA (Appendix A-2) is also a multi-factorial tool developed in France for

assessing the nutritional status of the elderly339. It has been shown to be useful in

assessing nutritional status of over 30 000 elderly subjects218, 340 from a variety of care


78

settings324, 341-343, including hospitalised elderly20, 37, 120, 121, 344. The characteristics of

MNA are summarised in Table 1-19. Compared to the SGA, the MNA is a more

objective tool as it is based on a scoring system for classification of individuals into

well-nourished or malnourished states. Many studies have evaluated the sensitivity,

specificity and reliability of the MNA in different settings and countries, mainly in

Europe and the US340. The MNA classification has been shown to be predictive of

mortality risk among elderly patients in acute and sub-acute care20, 37, 120, 121, 323.

Despite its predictive capability, there are several limitations associated with the

application of the MNA in the acute care setting (Table 1-16).

It is recommended that the MNA be validated for use in each country345 since the

anthropometric and BMI classifications in the MNA are based on French elderly and

it is known that anthropometric measures vary between ethnic groups346. However,

there are limited studies conducted using the original MNA among Asian elderly

populations78, 117, 124, 125. One of the studies was conducted with hospitalised elderly in

China (n=184, mean age 68 years) using the original MNA with different cut-offs117.

When the MNA was applied to the hospitalised elderly patients, excluding those with

dementia, and validated against BMI (<18.5 kg/m2), improved sensitivity (from 64%

to 86%) of the MNA was only shown after changing the MNA total score cut-off

(from MNA score <17 to <19)117. Another study was conducted among Taiwanese

community elderly (n=661, aged >65 years) using a modified version of the MNA125.

Tsai et al (2009)125 modified the MNA by eliminating the BMI question and adopting

population-specific anthropometric cut-offs for mid-arm circumference (men<23.5cm,

women<22cm) and calf circumference (men<30cm, women<27cm). In their

evaluation of the nutritional status of community elderly (n=301, aged >65 years) in


79

Taiwan, they showed the modified MNA has improved discriminating ability

compared to the original MNA125. However the MNA was not validated against other

criteria, and the sensitivity and specificity were not reported.

Only one published study was conducted in Singapore among 154 nursing home

residents (mean age+SD: 77+12 years) using the original MNA78. The MNA (score

<17) was not shown to be predictive of mortality up to 2 years in this study after

adjustment for important covariates i.e. age, gender, functional impairment and

comorbidities. This could be explained by the fact the MNA includes questions for

global assessment which would have interaction with the patient’s functional status

and comorbid illnesses. Nonetheless, the MNA was able to identify malnourished

residents (MNA<17) with reduced survival times78.

From the literature reviewed, validation of the original MNA among hospitalised

Asian elderly are still limited and there is insufficient evaluation done in Singapore.

Since the MNA is such a widely used and reported nutrition assessment tool in the

elderly across care settings, it is important to evaluate if it would be equally practical

and valid in acute care Singaporean older adults.


80

Table 1-19: Comparison of characteristics between Subjective Global Assessment (SGA) and MiniNutritional Assessment (MNA)

Characteristics SGA 235, 320 MNA 339 Features

Clinical history • Weight change • Dietary intake change • Gastrointestinal symptoms • Functional capacity • Disease and its relation to nutrition requirements

Physical examination • Loss of subcutaneous fat • Muscle wasting • Oedema and ascites

Anthropometric • weight, height, arm and calf circumferences, weight loss

General • lifestyle, medication, psychological, mobility status

Dietary • number of meals, food and fluid intake, ability to self-feed

Subjective • self-perceived quality of both health and nutrition.

Include specific characteristics of older population

Classification of nutrition status

Categories • Well-nourished (SGA A) • Moderately malnourished (SGA B) • Severely malnourished (SGA C)

Objective categories on a 30 point scale • Well-nourished (24-30 points) • At risk of malnutrition (17-23.5 points) • Malnourished (<17 points)

Original purpose of tool

Predict nutritionally associated complications in patients undergoing gastrointestinal surgery 327

Evaluate risk of malnutrition in frail elderly and identify those who could benefit from early intervention

Applied to patient types

Renal318, 328, 329, Liver transplant 347, HIV330, 331, Geriatric95, 113, 129,

203, 333, Oncology332, Surgical327

Wide range of settings : Elderly in nursing homes, community, hospitals (acute and sub-acute)

Use as screening tool No Yes Diagnostic tool for nutritional status

Yes Yes

Prognostic tool for outcomes (Predictive validity)

Yes; mortality 334, major postoperative complications 327 Yes; mortality 37, 120

Training required Yes Not stated User Healthcare professional

Healthcare professional


81

Table 1-19: Comparison of characteristics between Subjective Global Assessment (SGA) and MiniNutritional Assessment (MNA)

(MNA) (continued)

Characteristics SGA 235, 320

MNA 339

Inter-rater agreement Good >80%, kappa >0.7 338

Kappa 0.8 348

Sensitivity; Specificity 82%; 72% in predicting nutritionally associated complications 320 85%; 68% in the elderly 93

96%; 98%218

Convergent validity High correlations with anthropometry, albumin, total serum protein, weight, weight loss 319, 321 97

Correlated well with nutritional intake and with anthropometric (BMI, TSF, MAC, CC) and biochemical (serum albumin) parameters 310, 340

Validation studies in elderly

Yes Yes

Advantages • No biochemical data required • Non-invasive • Simple and accurate • Good for detecting established malnutrition

• No biochemical data required • Non-invasive • Most widely used tool • Able to detect specific problem areas from the individual

questions in the tool, allowing the clinician to target these areas and design specific plans for nutritional intervention 340

• Could be used as a follow up nutritional evaluation tool • Better at detecting patients requiring preventive nutrition

measures 129

Limitations • Does not allow for the categorization of mild malnutrition as it focuses on chronic rather than acute nutritional changes 334

• Not useful for early detection of malnutrition • Not practical to use for follow-up and monitoring during

nutritional support 97

• Require anthropometric measurements • Anthropometric and BMI classifications based on French

elderly, do not take into account ethnic variations • No provisions for assessing patients in tube feeding • Too many questions cannot be completed in patients, leading

to increased subjectivity 349

Abbreviations: BMI: body mass index; CC: calf circumference; MAC: mid arm circumference; TSF: triceps skinfold thickness;


82

1.8.6 Clinical applications of nutrition parameters

The various nutrition parameters discussed in this section 1.8 are compared with

respect to their clinical applications and shown in Table 1-20. Anthropometric

measures could vary in their specificity for diagnosing malnutrition as they are

affected by ageing and diseases associated with hospitalisation279. They tend to lose

their specificity in the sick adults315. For example, muscle wasting in bedridden

patients could happen regardless of their nutritional status. CAMA is the only

anthropometric measure which is able to differentiate between muscle and non-

muscles loss and is less influenced by changes in fluid status. However, limited

studies have evaluated the use of CAMA in hospitalised elderly.

Despite some of the limitations in arm anthropometry, they remain relatively easy to

measure in hospitalised patients. Although BMI is often difficult to obtain and there

are no consensus for an appropriate cut-off, it is nevertheless the most measured

parameter used to define malnutrition. Multi-parameter tools such as the SGA or

MNA may be more superior to anthropometry in their ability to define malnutrition

and predict adverse clinical outcomes. The clinical applications of the parameters

reviewed have not been validated in Singapore hospitalised older adults and their

potential usefulness in this population needs further evaluation.


83

Table 1-20: Comparisons on the applications of commonly used nutrition parameters in clinical practice

Easy or non-

invasive measure

Predictive validity

Validated and useful in hospital

adults

Validated and useful in elderly

Malnutrition diagnosis

Monitor of nutritional

status

Used singly

Validated in Asian

population

Anthropometry Weight � - � � - � � - Weight loss � � � � � � � � Knee height � - � � � � � Limited Body mass index � � � � � � � Limited Triceps skinfold thickness � � � � � � � � Mid-arm circumference � � � � � � � � Mid-arm muscle circumference

� � � � � � � �

Mid-arm muscle area � � � � � � � � Corrected arm muscle area � � � � � � � � Biochemical Serum albumin � � � � � � � � Total lymphocyte count � � � � � � � � Dietary � - � � � � � - Clinical � - � � � � � - Multi-parameter Subjective Global Assessment

� � � � � � � �

Mini Nutritional Assessment

� � � � � � � Limited


84

1.9 Summary of research gaps

Prevalence and consequences of malnutrition

Although the high prevalence and extensive consequences of malnutrition in

hospitalised older adults has been widely documented in the Western countries, there

are limited publications in English on malnutrition prevalence among elderly in Asian

countries such as Singapore (section 1.5). From the earlier preliminary data, the

malnutrition risk and prevalence among the Singaporean older adults in the

community56 and long-term care78 appears to resemble that of the Western countries.

However evidence on the extent and impact of the malnutrition problem among

Singapore hospitalised elderly is lacking, and the characteristics associated with the

risk of malnutrition are also not well-documented. This area of research should not be

neglected further due to the associated adverse clinical outcomes discussed (section

1.6) and in a rapidly ageing Singapore population.

Despite the available evidence on the associations between malnutrition and adverse

clinical outcomes, there were few well-designed longitudinal studies in the literature

reviewed that reported the independent relationship between them after adjustment for

a wide range of potential covariates e.g. age, cognition, functional status, severity of

disease, co-morbidities, on clinical outcomes (section 1.6).

Besides establishing the prevalence of admission malnutrition, determining the

prevalence of nutritional decline during admission is equally important since

hospitalised older adults are vulnerable to nutritional deterioration. At present, there

are limited reports in the literature which studied the change in nutritional status

during hospitalisation and its impact on clinical outcomes (section 1.5.4).


85

With more evidence and better understanding of the malnutrition issue, appropriate

prevention, identification and intervention programmes can be planned and

implemented to tackle this problem in Singapore. It has been shown by the Cochrane

review that malnourished elderly patients benefited most from energy protein

supplementation169. In Singapore, oral nutrition supplements are commonly

prescribed for older patients with poor oral intake.

Identification of malnutrition: nutrition screening and assessment

Even with the evidence on the prevalence and consequence of malnutrition in Western

countries, it is often under-diagnosed and under-treated59, 134, 350. This could be partly

attributed to the absence of a “gold standard” for defining nutritional status122, 351 and

a lack of nutrition screening policy. It is therefore crucial that validated nutrition

screening and assessment tools are used for early identification of malnutrition.

Many different combinations of criteria, different cut-offs, and the use of different

assessment methods250, 352 have been reported and shown to influence the variability

in malnutrition prevalence (section 1.5). There is however a lack of a universally

accepted nutrition assessment method which is deemed the most accurate and valid

for identifying malnutrition225 (section 1.8). Common nutrition assessment indices

and tools such as BMI, CAMA, SGA and MNA have limited evaluation on their

prognostic validity in the hospitalised older adults, and have been conducted mainly

amongst Caucasians. Moreover few studies have compared the validity of the

different nutrition assessment methods within the same sample.


86

Likewise, many of the nutrition screening tools available were evaluated and

validated in adult Caucasian populations and few have been validated specifically in

hospitalised older adults. In these validation studies, different methods of validation

have been applied making comparison of the tools difficult (section 1.7). Although the

most common approach is to evaluate the criterion validity (sensitivity and specificity)

of the screening tool against a reference method for malnutrition assessment, the lack

of consensus for malnutrition assessment method has influenced the variability in the

selection of the validation reference. Moreover, few validation studies are designed

with blinded assessors for screening and assessment, and the comparison of several

screening tools in a single study is also uncommon.

Although it is recognised that nutritional evaluation should form an important

component in the overall clinical assessment of the hospitalised elderly patients, little

is known about which methods of screening and assessment are most appropriate for

use in Singapore. Even though a screening tool (TTSH NST) has been developed for

the adult inpatients in TTSH, it is not known if it is equally valid in the older patients

and how it compares with the other more established screening tools in the literature.

Therefore further validation and comparisons of the screening tools in Singapore

hospitalised older adults would be useful to identify a valid and suitable tool that

could be adopted for use in this population. Likewise, the SGA is being used routinely

in TTSH to assess the nutritional status of those at risk of malnutrition. However, it

has not been validated in the older patients in Singapore or compared with other

assessment methods.


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The validity, application and usefulness of common screening and assessments tools

are rarely reported in the Asian elderly. Due to the multi-ethnicity, cultural, and

language differences in Singapore older adults compared to the other non-Asian

countries, the results from other validation studies may not be applicable. Therefore it

is important to identify validated population and setting specific nutrition screening

and assessment methods to accurately detect and diagnose malnutrition in Singapore.

This will allow more accurate identification of malnutrition, and minimise the under-

diagnosis and under-treatment of malnutrition. The problem of malnutrition can then

be effectively managed, coupled with appropriate intervention plans.


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1.10 Research aims and questions

This research study was designed therefore to better understand the extent of

malnutrition among hospitalised elderly in Singapore, and to determine suitable

methods for identification of malnutrition. The aims of the study are as follow:

1. To determine the sociodemographic indicators, nutritional, appetite, chewing,

swallowing status, and clinical characteristics of older adults on admission to a

Singapore acute hospital, according to age.

2. To determine the malnutrition prevalence, as defined by four malnutrition

assessment methods (SGA, MNA, BMI, CAMA), of older adults on admission

to a Singapore acute hospital and their associated characteristics.

3. To establish the predictive ability of the four malnutrition assessment methods

(SGA, MNA, BMI, CAMA) with respect to clinical outcomes (e.g. length of

stay, discharge to higher level care, 3-month hospital readmissions, and 6-

month mortality and functional status), and determine the one most suitable

for use in this population.

4. To validate four nutrition screening tools (TTSH NST, NRS 2002, MNA-SF,

SNAQ©) against the malnutrition assessment method identified in the study

and determine the one most suitable for use in this population.

5. To examine the changes in anthropometric measurements and nutritional

status of hospitalised older adults during admission and the impact of reduced

nutritional status on clinical outcomes.

In a specialist geriatrics unit in an acute care hospital in Singapore, the following

research questions are examined in patients admitted to the unit:


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Research Question 1:

What are the sociodemographic indicators, nutritional, appetite, chewing,

swallowing status, and clinical characteristics, according to age, of older adults

upon hospital admission?


What is the prevalence of malnutrition, as defined by four different malnutrition

assessment methods (SGA, MNA, BMI, CAMA), of older adults upon hospital

admission and its associated characteristics?


What are the predictive abilities of the four malnutrition assessment methods, and

which one is most suitable for use in hospitalised older adults?


Which of the four nutrition screening tools has the best validity in predicting

malnutrition when compared against the identified malnutrition assessment method

(from Research Question 3) in the study, and hence most suitable for use in

hospitalised older adults?


What are the changes in anthropometric measurements and nutritional status o older

adults during hospital admission and the impact of reduced nutritional status on

clinical outcomes?

Details of the study design and methods are presented in the next chapter.


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2 Methods

This study protocol was approved by the Ethics Review Board of Tan Tock Seng

Hospital on 1 Feb 2006 (DSRB-A/06/030), and the Human Research Ethics

Committee of Queensland University of Technology on 9 Oct 2006. The study was

supported by a research grant of S$81, 000 (SIG/06051) from the National Healthcare

Group (NHG) in Singapore, on which the PhD candidate (YP Lim) was the Principal

Investigator.

2.1 Sample type and size

2.1.1 Sample power

To capture the anticipated malnutrition prevalence of 30% based on rates reported in

other studies (Table 1-6) with a 95% confidence interval of 25-35%, a target sample

size of 300 was required. Power calculations were not done for any other analyses in

the study. However the sample size selected was consistent with that used in other

similar studies (Table 1-6 and 1-7).

2.1.2 Sample recruitment

All inpatients consecutively admitted to three Geriatric Medicine wards in Tan Tock

Seng Hospital during the 8-month study period (November 2006-July 2007) and those

aged >60 years were eligible for inclusion into the study. Exclusion criteria included

patients who were:

• terminally ill or under palliative care service

• critically ill or classified as dangerously ill (DIL) by the doctors


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• discharged within 72 hours admission

• admitted for more than 72 hours and still unable to obtain consent

• previously recruited into the study and readmitted

The Principal Investigator (YP Lim) was responsible for obtaining all the study

consents. Written informed consent was obtained from the patient or their family

members if they were cognitively impaired. If patients were illiterate or did not speak

English, the study was explained in the language of their choice such as Mandarin or

various dialects, and signed by patient and witnessed by a staff nurse. All eligible

participants were recruited within 72 hours of admission.

2.2 Measurements

The study was conducted in 2 phases. Phase 1 was cross-sectional with all the

baseline characteristics obtained and assessed, including all nutrition screening and

assessment. Phase 2 was a prospective follow-up of clinical outcomes at discharge, 3-

months and 6-months post discharge.

Phase 1

All participants had the following details recorded and measures taken (Table 2-1)

within 72 hours of their admission.

2.2.1 Nutritional screening

A single dietetic technician (dietitian assistant), who was trained by the dietitian (YP

Lim) completed the four nutrition screening tools353 (TTSH NST, NRS 2002, MNA-


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SF and SNAQ© ) for every patient within a single session, in no particular order of

administration (Appendix: Data forms). To limit any bias in scoring, the final scores

for each tool were not computed until data were entered into the statistical software

for analysis. The details of each of these tools have been presented and discussed in

section 1.7.2. The tools were administered according to the respective guidelines

provided for each published tool. The dietetic technician obtained information from

the participants and/or their primary caregiver and the medical notes. A single

assessor was used to avoid the possibility of inter-observer variation. As some of the

components of screening tools are similar (e.g. weight loss), it was thought to be

easier administratively for a single assessor to complete all the tools to avoid

participants being interviewed asked similar questions repeatedly (e.g. weight loss).

As each of the nutritional screening tools is based on quantitative and objective

questions, the result of one assessment was independent of the outcome of the other.

The TTSH NST (Appendix Table A-4) was selected as one of the screening tools for

evaluation and validation as it was developed at the TTSH, and is currently routinely

used by the nurses on all patients at admission. However validity of the TTSH NST in

hospitalised older adults had never been evaluated. Since the TTSH NST was

routinely administered by the nursing staff, the screening performed by them was also

recorded from the participants’ clinical notes for comparison with that administered

by the dietetic technician in this study. This would also allow inter-rater reliability

assessment of the TTSH NST to be performed.

The selection of the NRS 2002 as one of the tools for validation was based on its

recommended clinical value and application in hospitalised patients166. In addition,


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the NRS 2002 was recommended by ESPEN as the malnutrition screening tool of

choice for the acute care setting200. Therefore it would be useful to compare its

validity with the TTSH NST as they both comprise similar components in the

screening tools.

The MNA- SF185 was chosen based on the widespread use of the MNA among the

elderly and the brevity of the shortened version. Although it was the only tool

developed specifically for older adults, its application and validity among the

hospitalised Asian elderly population223 had been less studied compared to the

Caucasian/Western population. Therefore it would be useful to compare the validity

between the elderly specific and non-elderly specific screening tools. The MNA-SF

has recently been updated and validated to include calf circumference as the

alternative screening question when BMI was not obtainable to improved its

applicability221, 222. However this study data were collected from November 2006 to

July 2007 prior to the introduction of the updated version of the MNA-SF in 2009.

Therefore, the original version of the MNA-SF was adopted and participants without

BMI measurements were excluded from completion of the MNA-SF.

Lastly, the SNAQ© 179 was selected because it is extremely short and easy to

administer as it consists of only three questions, none of which are reliant on BMI

measures. Although it was not developed specifically for the older adult population, it

was developed for hospitalised adults. A large proportion of the participants was

unsure or had unquantifiable weight loss in response to the question “Did you lose

weight unintentionally?” in this study and the original SNAQ© tool did not have the

option for scoring such responses. Therefore a modified SNAQ© screening tool was


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created in this study to allow for scoring these responses, which were important and

should not be ignored.

Although the MUST198 is a screening tool which was well validated particularly in the

UK, it was originally developed for use in the community and not specifically for the

elderly. It had been shown recently to be valid and predictive of outcomes among

hospitalised elderly202. However, weight and height measurements were limited

among the study participants and they were obtained mainly from recalls, recent

documentation or surrogate measures. It was anticipated that the completion rate for

the MUST would be low in this study population. Moreover, the three criteria within

the MUST (current weight status using BMI, unintentional weight loss over time and

an acute disease parameter) also form part of the TTSH NST. Therefore the MUST

was not chosen for evaluation in this study due to the anticipated difficulties in

completing the MUST and also its similarities with the TTSH NST.

The MST was also excluded in this study as it had been previously applied among the

hospitalised adults in TTSH and found to be less predictive of malnutrition (positive

predictive value 68%)60. Subsequently, the TTSH NST was developed based on

similar questions from the MST.

Therefore to keep the number of tools manageable in this study, only four of the six

tools reviewed were selected for validation. One developed in an Asian population

and used throughout the hospital, but not validated in the older adults (TTSH NST).

One developed and specifically recommended for older adults across all settings

(MNA-SF). One developed in Caucasian population and recommended for use in all


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hospitals (NRS 2002). One developed for use in hospital; short and not reliant on BMI

measures (SNAQ©).


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Table 2-1: Variables and measures collected at admission/baseline – Phase 1

Sociodemographic (obtained from medical records) • Age • Gender • Race (Chinese/Malay/Indian/Other) • Marital status (Single/Married/Divorced

or Widowed)

• Preadmission dwelling (Community/Institutionalised)

• Occupation(Retired/Working) • Smoking status (Yes/No) • Use of alcohol (Yes/No)

Clinical assessment Appetite, chewing and swallowing characteristics (self-reported): • Dentition (Good/Poor) • Dentures (Yes/No) • Fitting of Dentures (Well/Poor) • Swallowing impairment (Yes/No) • Modified texture diet (Yes/No) • Food consistency (Normal/Soft/Blended) • Thickened fluid (Yes/No) • Appetite (Good/Poor) • Taste change (Yes/No)

Clinical characteristics (medical records): • Dementia (DSM IV criteria354, Yes/No) • Depression (Yale-Brown Single

question tool355, Yes/No) • Delirium (Yes/No) • Pressure ulcers (Yes/No) • Hip fracture (Yes/No) • Cancer (Yes/No) • Number of prescribed drugs • AMT(abbreviated mental test) scores356 • Functional status for premorbid and on

admission (MBI*)162 • Charlson Comorbidity Index357 (CCI **) • Severity of Illness Index358 (SII**) • Biochemical • Serum albumin (ALB) • Total lymphocyte count (TLC)

Nutrition screening tools (performed by dietetic technician) • Tan Tock Seng Hospital Nutritional Screening Tool (TTSH NST) • Mini Nutritional Assessment-Short Form185 (MNA –SF) • Nutritional Risk Screening 2002166 (NRS 2002) • Short Nutritional Assessment Questionnaire179 (SNAQ©) Nutrition assessment (performed by dietitian) Anthropometry • Weight • Knee height • Triceps skinfold thickness (TSF) • Mid-arm circumference (MAC) • Calf circumference (CC) (part of MNA) Multi-dimensional tool • Subjective Global Assessment235 (SGA) • Full Mini Nutritional Assessment339

(MNA)

Derived measures • Body mass index (BMI) • Mid-arm muscle circumference

(MAMC) • Mid-arm muscle area (MAMA) • Corrected arm muscle area (CAMA)

*MBI completed by occupational therapist; **CCI and SII completed by doctor.

**performed by doctor and *occupational therapist


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2.2.2 Nutritional assessment

2.2.2.1 Anthropometry

2.2.2.1.1 Weight, Height, BMI

A single dietitian (YP Lim) completed nutritional assessments and measurements for

all participants to eliminate inter-observer error. Participants were weighed barefoot

wearing light clothing (hospital clothes). A single measure was taken using calibrated

chair scales (seca 954, Hamburg, Germany) and recorded to the nearest 0.1kg.

Participants who were bedbound, suffering from spinal or hip fractures, or unable to

sit on a wheelchair were excluded from weight measurements.

Height was predicted using knee height as the proxy measure in this study as most

participants were too ill to bear weight, or to stand upright steadily without risk of

falling. It had been shown that only 69 (14%) out of 484 acutely ill patients could

have their standing height measured359. Therefore standing height was not measured

for all participants as it was anticipated that there would be too many missing data.

Knee height was chosen as the surrogate method to standardise the estimation of

height in hospitalised elderly patients as it has better reliability compared with other

surrogate methods such as demi-span and arm span249.

Knee height was measured with sliding callipers (Ross Laboratories, USA) on the left

leg of each participant while supine or sitting, according to established protocol249. If

the left leg could not be measured due to amputation, fracture, or stiffness resulting in

inability to position the leg correctly, the right leg was measured. Knee height is the

distance from the sole of the foot to the anterior surface of the thigh with the ankle


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and knee each flexed to a 90 degree angle. One blade of a sliding, broad-blade calliper

was placed under the left heel, and the other blade was placed over the anterior

surface of the left thigh above the condyles of the femur and just proximal to the

patella. The shaft of the calliper was held parallel to the shaft of the tibia and pressure

applied to compress the tissue249. The reading was taken to the nearest 0.1 cm. The

estimated height was calculated using the predictive equation derived from 164

female and 89 male Chinese elderly aged >60 years in Hong Kong253 :

Predictive equation for height using knee height:

Men: Height (cm) = 51.16 + [2.24 x knee height(cm)]

Women: Height (cm) = 46.11 + [2.46 x knee height(cm)] – (0.12 x age)

This set of predictive equations was chosen instead of the other available equations

because the profile of the study population (Chinese elderly) in which this equation

was derived from closely resembled that of the participants from this study.

Body mass index (BMI) was calculated based on measured weight and estimated

height from knee height (weight/ estimated height2). A large range of BMI cut-off

values (17-23.5 kg/m2) have been recommended or applied in studies to identify

malnutrition107. According to one review, BMI cut-offs between 18.5 and 20.0 kg/m2

are most commonly used in clinical practice107. The optimal BMI range for Caucasian

older adults was suggested to be 24-29kg/m2 268. Although studies have reported and

suggested using higher BMI cut-offs such as <20kg/m2 or <22kg/m2 for older adults,

the associations between these BMI cut-offs and mortality showed inconsistent


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results146, 229, 242, 265, 266. On the other hand, studies conducted among Asians showed

that the acceptable range for BMI was lower at 18.5-23.0kg/m2 264, 269.

A BMI classification based on the assessment method of chronic energy deficiency

(CED) by James et al (1998)260 was adopted by WHO (Table 2-2). It supported a BMI

cut-off of 18.5kg/m2. Hence participants with BMI <18.5kg/m2 were classified as

malnourished, and those with BMI >18.5kg/m2 were classified as well-nourished in

this study.

Table 2-2: Classification of BMI based on WHO international classification258:

BMI Classification

<18.5 kg/m2 Underweight

18.5.-24.9 kg/m2 Normal weight

25.0-29.9 kg/m2 Overweight

>30.0 kg/m2 Obese

2.2.2.1.2 Arm anthropometry

All arm measurements were taken with the participants sitting upright, on their right

arms. The left arm was measured if the right arm had been altered by disease such as

hemiparesis, oedema or fracture. Mid-arm circumference (MAC) is a useful measure

of muscle protein stores281. The midpoint between the tip of the acromion and the

olecranon process was marked while the participant’s arm was bent at the elbow at a

right angle, and the forearm placed palm down across the body228. MAC was

measured with a ¼ inch width flexible (fibreglass) non-stretch tape (DMI, USA)

calibrated in centimetres while the arm was hanging freely beside the trunk or relaxed


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at the side with the hand resting on the right thigh360. The tape was tightened until it

was in contact with the skin without constricting it. One measurement was recorded.

The precision of measurement was + 0.1cm281.

TSF provides an estimate of body fat reserve273. After the MAC measurement, with

the arm remaining in the same position, TSF was measured. The triceps skinfold was

pinched between the thumb and index finger over the triceps muscle of the right arm,

2 cm above the marked midpoint, in line with the tip of the olecranon process228. The

crest of the fold was parallel to the long axis of the hanging arm. The skinfold was

gently pulled away from the underlying muscle tissue and the calliper jaws applied at

right angles, at the marked midpoint. The skinfold remained held between the fingers

while the measurement was taken228. The TSF was measured to the nearest 0.2mm

using a Harpenden skinfold calliper (Model: HSK-BI, British Indicators, Baty

International, UK). Duplicate readings were made to improve the accuracy and

reproducibility of the measurements. The mean of the two measurements was

recorded.

MAC and TSF were measured in the recumbent position when the participant was

unable to sit upright. The participant lay on their left side with legs bent and their

head supported by a pillow. The right arm rested along the trunk, with the palm

down228. It had been shown in previous studies that values obtained in both positions

were highly correlated 232, 361. MAC and TSF were the principal measures used to

calculate the derived measures such as mid-arm muscle circumference (MAMC),

mid-arm muscle area (MAMA) and corrected arm muscle area (CAMA) using

established formula (Table 2-3).


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Table 2-3: Derived arm anthropometric measures and associated equations

Mid-arm muscle circumference (MAMC)(cm)283 : MAC (cm)– {π x TSF*(cm)}

Mid-arm muscle area (MAMA)(cm2)283 : [MAC(cm) – {π x TSF(cm)}]2/ 4 π

: Men:[MAC(cm) – {π x TSF(cm)}]2/ 4 π – 10 Corrected arm muscle area (CAMA)(cm2)286

: Women: [MAC(cm)- {π x TSF(cm)}]2/ 4 π – 6.5

*TSF: triceps skinfold thickness

Male participants with CAMA <21.4cm2 and female participants with CAMA

<21.6cm2 were classified as malnourished286 and those with CAMA >21.4 cm2 and

>21.6 cm2 for male and female respectively were classified as well-nourished in this

present study. Although lower cut-off values for CAMA <16 cm2 or <5% percentile

have been applied and shown in other studies79, 277, 280 to predict mortality among

older adults, these cut-off values only identified those with more severe muscle

wasting. Therefore to also include those with moderate muscle wasting (moderately

malnourished), the present study adopted the higher cut-off values.

The anthropometric measurements (BMI, MAC, TSF, MAMC, MAMA, CAMA)

selected for use in this study are commonly used in research and clinical practice.

They have been shown to be useful and relevant clinically in assessing nutritional

status. BMI and CAMA were evaluated as anthropometric markers for defining

malnutrition in this study as they are most commonly used and shown to have better

predictive validity than other anthropometric measurements (refer to section 1.8.1

and Table 1-20). Details of each of these measures have been discussed in section

1.8.1.


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2.2.2.2 Multi-parameter tools – SGA and MNA

This study evaluated the Subjective Global Assessment (SGA)235 and Mini Nutritional

Assessment (MNA)339 as the multi-parameter tools for defining malnutrition because

they are widely used (refer to section 1.8.5). Details of the SGA and MNA and their

previous validation have been discussed in section 1.8.5. The SGA and MNA were

concurrently completed by a single dietitian (YP Lim) within 72 hours of admission

to determine malnutrition status, independent from the nutritional screening. To limit

potential bias in rating the participants’ nutritional status, the dietitian was blinded to

nutritional screening performed by the dietetic technician.

The technique of SGA was applied according to the published detailed guidelines by

Detsky235 to reduce subjective influences and arbitrariness in classification. The

assessor had previously received training on the use of the SGA and had been

performing the SGA on her patients regularly in her clinical practice. The SGA rating

A (well-nourished) was classified as “well-nourished”, and SGA rating B (moderately

malnourished) and C (severely malnourished) were combined into a single category

“malnourished” due to small numbers, for analysis and discussion in this study. For

comparisons between the different nutritional assessment indices and tools and to

standardise the statistical analysis, the classification of nutritional status is

dichotomised.

The full MNA was completed according to the guidelines published on the Nestle

Clinical Nutrition website360. For participants without weight, knee height or BMI

measures, their MNA was considered incomplete and excluded from all data analysis.

Participants with a MNA score <17 (malnourished) were classified as “malnourished”


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in this study31, 33. The MNA “well-nourished” (score >23.5) and “at risk” (score 17-

23.5) categories have been combined into a single category “well-nourished” for

subsequent analysis as a clear distinction is needed to be made between well-

nourished and malnourished participants and the “at risk” participants were not yet

malnourished effectively. Similar to the SGA, the classification of nutritional status

by the MNA is dichotomised.

As it was not feasible or practical to have two different assessors to perform the SGA

and MNA independently due to increased response burden on the participants, steps

were taken to minimise the single assessor bias. The SGA, a more subjective

assessment tool without a scoring system, is more likely to be influenced by the

outcomes of other assessment tools as it is based on the assessor’s clinical judgement.

Therefore to minimise the influence from the total MNA score, the SGA was

completed with the final rating prior to MNA score computation. The total score of

the full MNA was only calculated during data entry.

Participants who were cognitively impaired could not respond accurately to interview

questions during the assessment. They were not excluded from the study as they

constituted a substantial proportion of the hospitalised elderly and they were usually

at higher risk of poor nutrition31, 37. Therefore, information and history were obtained

from their primary caregivers. This approach is appropriate as it has been shown that

health information obtained from proxy who lived with elderly respondents were in

good agreement with self-reports362. For participants whose reliability was

questionable, assessments were confirmed with their family members.


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Important information relating to nutritional status, particularly characteristics such as

presence of swallowing impairment, appetite and taste changes, use of modified

consistency diet, level of dentition (good or poor), use and fitting of dentures were

also collected from self-report by participants or their families.

2.2.2.3 Biochemical – ALB and TLC

Serum albumin (ALB) levels and total lymphocyte counts (TLC) were obtained from

the participants’ laboratory report when bloods for standard inpatient care were taken

within 72 hours upon admission. Participants were not subjected to additional blood

taking due to participation in the study if ALB were not ordered. TLC was calculated

from the product of serum white blood cell levels (WBC) and percentage lymphocyte

count.

These two biochemical tests (ALB and TLC) were selected for inclusion in this study

as they were the most common nutrition-related blood analysis done routinely for

newly admitted patients to geriatric medicine in TTSH. Although both measures were

potentially useful indicators of nutritional status62, 280, 284, 308, their levels may often be

influenced by other clinical conditions during hospitalisation (discussed in section

1.8.2). Therefore participants were categorised into ALB <35mg/L vs >35mg/L61, 306,

363, 364, and TLC <1.5x103/mm3 vs >1.5x103/mm3 208, 363, 364 and compared against the

malnourished and well-nourished participants classified using the SGA, MNA, BMI

and CAMA.


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2.2.3 Clinical assessment of covariates

2.2.3.1 Dementia and depression

Diagnoses or indicators of depression and dementia were obtained from participants’

case records. These assessments were performed routinely by the attending doctor

upon admission or as part of the patient’s initial assessment. Dementia was diagnosed

based on DSM IV criteria354. Depression was screened using a single question tool –

the Yale-Brown obsessive compulsive scale – “Do you often feel sad or

depressed?”355, which had been shown to be an efficient diagnostic strategy among

older Chinese patients to identify depression365.

2.2.3.2 Functional status (premorbid and on admissi on)

The participants’ premorbid and on-admission functional status were assessed using

the Modified Barthel Index (MBI)162 by the ward occupational therapists. The MBI is

a validated tool used to evaluate the level of assistance required for activities of daily

living including personal hygiene, bathing, feeding, toileting, stair climbing, dressing,

bladder/bowel control, chair/bed transfers and ambulation or wheelchair operation

(Table 2-4). The tool can be administered by any health care professional using direct

examination or discussion with the patient and/or their family members. Scoring is

based on a continuous scale between 0 and 100, with 100 indicating independent

function. Classification of dependency needs based on MBI score are shown in Table

2-5.


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Table 2-4: Modified Barthel Index162

Item Fully independent

Minimal help

required

Moderate help

required

Substantial help

required

Unable to perform

task Personal hygiene 5 4 3 1 0 Bathing 5 4 3 1 0 Dressing 10 8 5 2 0 Feeding 10 8 5 2 0 Toilet 10 8 5 2 0 Bowel control 10 8 5 2 0 Bladder control 10 8 5 2 0 Stair climbing 10 8 5 2 0 Ambulation 15 12 8 3 0

Wheelchair * 5 4 3 1 0 Chair/Bed transfer 15 12 8 3 0

* Score only if ambulation coded "1" and patient trained in wheelchair management Source: Shah et al, 1989162

Table 2-5: Classification of dependency levels used by the Modified Barthel Index

Categories MBI total scores Dependency level

1 0-20 Total

2 21-60 Severe

3 61-90 Moderate

4 91-99 Slight

5 100 Independent

MBI: Modified Barthel Index

Source: Shah et al, 1989162


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2.2.3.3 Measure of comorbidity

A single doctor assessed the comorbidity indices using the Charlson Comorbidity

Index (CCI)357 for risk adjustment i.e. controlling for participants' baseline risk for

health outcomes. The CCI is a widely used and studied weighted index that classifies

comorbid conditions (Table 2-6) to predict the risk of mortality from comorbid

diseases for use in longitudinal studies. It takes into account the number and the

seriousness of comorbid diseases. It is a simple, readily applicable and valid method

for assessing comorbidity. Higher scores indicate a more severe condition and

prognosis.

Table 2-6: Charlson Comorbidity Index357

Charlson Comorbidity Index Score Myocardial infarction 1 Congestive cardiac failure 1 Peripheral vascular disease 1 CVA (except hemiplegia) 1 Dementia 1 Chronic pulmonary disease 1 Connective tissue disease 1 Ulcer disease 1 Mild liver disease 1 Diabetes (without complications) 1 Diabetes with end organ damage 2 Hemiplegia 2 Moderate or severe renal disease 2 2nd solid tumour (non-metastatic) 2 Leukaemia 2 Lymphoma, multiple myeloma 2 Moderate or severe liver disease 3 2nd Metastatic solid tumour 6 AIDS 6 Age score (50-59 = 1, 60-69=2, 70-79=3, 80-89=4,90-99=5, 100-109=6) Total score Charlson score (age adjusted )

Source: Charlson et al, 1987357


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2.2.3.4 Illness severity

Severity of illness was classified using the Modified Severity of Illness Index (SII).

(Appendix: Modified SSI, 2004). Wong et al modified the original SII358 into a 4 level

(increasing severity from level 1 to 4) burden of illness measure (Table 2-7). The

modified SII was shown to have a high inter-rater agreement (kappa 0.80) and was

significantly associated with increased mortality and LOS in multivariate analysis.

Since the modified SII is a reliable and valid measure of illness severity in

hospitalised elderly patients from TTSH, it was adopted in this study. The medical

records of participants were reviewed by a single doctor and the SII measures of the

principal diagnosis and each additional diagnosis were scored from level 1 to 4. The

overall illness severity for each subject was determined using the most severe score

from the combined principal diagnosis and additional diagnosis list.

Table 2-7: Modified Severity of Illness Index (SII)

The 4 levels of the modified SII:

Level 1 Asymptomatic

Level 2 Symptomatic but vital signs are not affected

Level 3 Presence of any one of the following:-

Systolic blood pressure >100mmHg,

Heart rate >100bpm, temperature >390C,

Oxygen requirement> intranasal 2L/min,

Nil orally >24hours

Level 4 Intensive care unit or high dependency unit admission


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2.2.4 Phase 2 (Follow-up)

Phase 2 was the prospective study phase on clinical outcomes upon discharge, at 3-

months and 6-months post discharge (Table 2-8). The dietitian (YP Lim)

retrospectively reviewed participants’ case records upon discharge to record the

relevant clinical outcomes.

2.2.4.1 Change in nutritional status

On discharge, weight, MAC, TSF and SGA were reassessed in the same way by the

same dietitian (YP Lim) as when they were done on admission. Discharge MAMC,

MAMA and CAMA were also calculated. Change in weight, MAC, TSF, MAMC,

MAMA and CAMA from admission to discharge were calculated and compared.

Percentage weight loss was computed individually in participants where both

admission and discharge weights were measured. Weight loss of >5% in 30 days or

>10% over 6 months have been considered clinically significant26, 235, 236 and

associated with poor clinical outcomes233, 237. However due to the relatively short

hospital stay (average LOS of GRM patients is 11 days) of the participants in the

present study, weight loss >1% per week was considered clinically significant236, 366,

and used to define decline in nutritional status. Percentage weight loss per week was

calculated using the equation: (weight loss during hospitalisation/admission weight) x

(7 days/LOS) x 100%. The other arm anthropometric measurements were not

selected to define change in nutritional status as no similar cut-offs are available or

have been reported in previous studies.

MNA and BMI were not repeated at discharge as the completion rates were relatively

lower on admission due to the lack of weight measurements. The low completion


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rates were expected to remain so on discharge. Moreover the scoring for some of the

questions in MNA e.g. those nutrition and subjective components are prone to rapid

variations for short duration of hospitalisation, and more so if the nutritional problem

is managed20. Other components in MNA which related to acute illness and treatment

tend to improve with time20. Hence repeating the MNA may not specifically reflect

change in nutritional status during admission.

2.2.4.2 Referral to dietitian for treatment of maln utrition

To determine the referral to dietitian for treatment of malnutrition in the hospital

based on standard usual care, records for the referrals and dietitian assessments were

reviewed from the clinical notes at discharge. These dietitian referrals were initiated

through the hospital standard protocol, either from the nutrition screening process or

from doctor’s referral based on their clinical assessments. This study did not trigger

any dietitian referral regardless of the results of the nutritional screening and

assessment since there was an existing hospital protocol. The level of malnutrition

recognition (by nurses) was evaluated by comparing the proportion of those identified

as at risk upon admission through the routine nutrition screening, against the total

malnourished participants (assessed by SGA in the study). The frequency of dietitian

referral was expressed as a proportion against the frequency of those who were

malnourished (defined by SGA), as well as those with reduced nutritional status

(weight loss >1%) during admission.

Day of referral from admission was recorded to determine the timeliness of the

referral to a dietitian. Timeliness was computed using the median time to dietitian

referral after hospital admission, and comparisons were made, using the Mann-


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Whitney test., between the well-nourished and malnourished participants, and

between those with and without decline in nutritional status.

2.2.4.3 Clinical outcomes

Length of stay (LOS) was calculated from date of admission till date of discharge.

Participants were classified into LOS >11days to represent longer LOS vs <11days

for shorter LOS for analysis of clinical outcomes. The cut-off of 11 days was used as

the average LOS of patients from Geriatric Medicine in TTSH was 10.9 days (TTSH

Intranet, 2006).

The discharge destination of each participant was recorded and compared with the

living situation prior the hospital admission. Participants were considered discharged

to a higher level care if they were not discharged back to their usual living

arrangements but was transferred to institutionalised care such as a community

hospital or nursing home instead. For example, participants who were admitted from

their own home and were discharged to a community hospital or nursing home, or

admitted from community hospital and discharged to a nursing home, were

considered as discharged to higher level care.

All the participants were followed up at 3-months post discharge for readmission rates

and at 6-months post discharge for mortality and functional status through phone

interviews by the dietitian (YP Lim) (Table 2-8). All information was obtained either

through self-reporting or proxy reports. The participants’ readmission information

was confirmed from the hospital’s patient information system. The functional status at

6-months post discharge was assessed using MBI based on self-reported information


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by the participants or their proxies. MBI score obtained from telephone interviews

have been shown to be highly correlated with those assessed through direct

examinations367, therefore this follow-up approach was applied in this study. For

participants who were deceased at the follow-up phone interview, the family were not

questioned further for the exact date of death as it was deemed inappropriate.

Table 2-8: Variables and measures collected upon discharge and post discharge –

Phase 2

Upon discharge 3 months follow-up 6 months follow-up

Clinical outcomes

Length of stay

Discharge destination


Weight

Mid-arm circumference

(MAC)

Triceps skinfold thickness

(TSF)

Subjective Global

Assessment (SGA)

Hospital readmission

Mortality

Self-reported functional

status (MBI)

For participants who were cognitively impaired, primary caregivers who provided the

information during the admission assessment were contacted. For participants

discharged to institutionalised care (i.e. nursing home or rehabilitation/sub-acute

hospital), the respective nursing staff-in-charge was contacted for information. It was

logistically difficult to arrange for participants to attend follow-up appointments in

clinic solely for the purpose of research as not all participants had follow-up

appointments in the hospital outpatient clinics upon discharge. The default rate was


113

anticipated to be high if a follow-up appointment was required. In addition there were

no funds to support the costs of return visits for assessments; therefore phone

interview was the preferred approach.


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2.3 Statistical Analysis

All analyses were performed using SPSS 13.0 with statistical significance set at

p<0.05. The list and types of variables are presented in Table 2-9.

To characterise the study participants, the following analyses were performed:

• Normality for continuous variables was checked using Komolgorov Smirnov 1

Sample test coupled with histogram, and also checked for mean+3SD to be within

the observed minimum and maximum values.

• Descriptive statistics were used to describe the baseline characteristics

(sociodemographic, nutritional, appetite, chewing, swallowing, and clinical) and

clinical outcomes of the study participants. Continuous outcome variables were

expressed as mean+SD and median (range) for normally and non-normally

distributed variables respectively. Categorical variables were expressed as

frequencies with percentage of total in brackets.

• All participants’ characteristics were presented for the total sample and for the

different age groups: 60-75years, 76-85 years and >85 years.

• The comparisons of characteristics between the three age-groups were performed

using parametric tests i.e. analysis of variance (ANOVA) for continuous variables,

when normality and homogeneity assumptions were satisfied. Otherwise the

equivalent non-parametric test, the Kruskal Wallis test, was applied. Post-hoc

Bonferroni tests were performed for variables which were significantly different

between the 3 groups to identify where differences lay. Chi-square tests were

applied for categorical variables. P values were presented for all statistical

comparisons of the variables between the different age groups.


115

The results from the above analyses are presented in Chapter 3.

To determine the nutritional status and characterise the study participants by their

nutritional status, the following analyses were performed:

• The nutritional status of study participants were classified into well-nourished and

malnourished according to the four different malnutrition assessment methods

(SGA, MNA, BMI and CAMA). The prevalence of malnutrition using different

assessment methods was compared and kappa estimates were derived to evaluate

the agreement of malnutrition assessment between the methods.

• Baseline characteristics of the well-nourished and malnourished participants (for

all four methods of malnutrition assessment) were compared.

• The comparisons were performed using parametric tests. Independent samples t

test for continuous variables, when normality and homogeneity assumptions were

satisfied. Otherwise the equivalent non-parametric test, the Mann-Whitney test,

was applied.

• Chi-square tests were applied for comparisons between categorical variables and

nutritional status, to identify variables associated with malnutrition.


To establish the prognostic validity of the four malnutrition assessment methods,

nutritional status was compared against the clinical outcomes with and without

adjustment for covariates and analysed.

• To determine the univariate associations between nutritional status (SGA, MNA,

BMI and CAMA) and the clinical outcomes, logistic regression analysis was

performed for each of the dichotomous outcomes (length of stay >11days,


116

discharge to higher level care, 3-month readmission, and 6-month mortality).

Linear regression was performed for continuous outcome variables (length of stay

and 6-month MBI). Odds ratio, Β (unstandardised coefficient), 95% confidence

interval and p values are presented.

• To determine the multivariate associations between nutritional status (as assessed

by the different nutritional assessment indices and tools) and the clinical

outcomes, multiple logistic regression analysis was performed for the

dichotomous outcomes, with inclusion of the covariates. Multiple linear

regression analysis was applied for the continuous outcome variable (LOS and 6-

month MBI). Since the exact date of death was not available, Cox regression

analysis and Kaplan-Meier survival curves cannot be performed with the 6-month

mortality data. Odds ratio, Β (unstandardised coefficient), 95% confidence

interval and p values are presented, where applicable.

• Since no “gold standard” exists for the assessment of malnutrition, and

malnutrition is associated with poor clinical outcomes, the various nutritional

assessment indices and tools were validated against the clinical outcomes. To

determine and compare the predictive power for each of the malnutrition

assessment methods on the clinical outcomes, each of the logistic regression

models were assessed. There were four nutrition models for each outcome. The

probabilities from each set of logistic regression models were then applied against

each clinical outcome using Receiver Operating Characteristic (ROC) curve

analysis. Area under the curve (AUC), sensitivity, specificity, positive predictive

value (PPV) and negative predicted value (NPV) were presented and compared.

• The malnutrition assessment method which exhibited the best predictive power

(i.e. highest) AUC for the most number of outcomes, and associated with the most


117

number of outcomes at the univariate and multivariate analysis was selected as the

reference measure for nutritional status.


To evaluate and select the most suitable nutrition screening tool, validity and

diagnostic performance of the four nutrition screening tools used in this study were

compared.

• Classification of nutritional risk according to the different nutrition screening tool

was compared. Comparisons also included the TTSH-NST completed by the

nurses and the modified SNAQ© tool.

• ROC curve analysis was performed for each of the nutrition screening tools

against the reference measure for nutritional status (identified from the earlier

analysis and presented in Chapter 4). AUC for each of the tools was presented.

The optimal cut-offs for each of tool was determined and compared to the original

cut-offs.

• A 2 X 2 table was set up for the classification of nutritional risk by the nutrition

screening tools against the classification of nutritional status by the reference

measure to determine the diagnostic performance. Sensitivity, specificity, positive

predictive value (PPV) and negative predicted value (NPV) were presented.

• Inter-rater reliability of TTSH NST between dietetic technician and nursing staff

was assessed using kappa estimates.

• The nutrition screening tool with the best diagnostic performance was then

selected for further evaluation of its prognostic performance. The nutritional risk

as defined by the screening tool was compared against all the clinical outcomes in

multiple logistic and linear regression analysis with and without adjustment for


118

covariates (age, race, dementia, depression, Severity of Illness, Charlson

Comorbidity Index, number of prescribed drugs and admission MBI). Odds ratios,

Β (unstandardised coefficient), 95% confidence intervals and p values are

presented where applicable.


To examine the change in nutritional status of older adults during hospital admission,

nutritional assessments on admission and upon discharge were compared and

analysed.

• Anthropometric measures i.e. weight, MAC, TSF, MAMC, MAMA and CAMA

on admission and upon discharge were compared using paired samples t-test when

normality and homogeneity assumptions were satisfied. Otherwise the equivalent

non-parametric test (Wilcoxon Signed Ranks test) was applied. For comparison

between the SGA classification of nutritional status on admission and upon

discharge, McNemar test was performed. P values are presented in all analyses.

• Descriptive statistics were used to compare participants who experienced decline

in each of the anthropometric measures. The median absolute and percentage

change from admission for each of the anthropometric measurements were

calculated among these participants (on a pair-wise basis) during admission.

• Change in nutritional status during hospitalisation was defined by weight loss

>1% per week. Only participants with both admission and discharge weight

measures were included in all the data analyses related to weight loss.

• Chi-square tests were applied to identify appetite, chewing, swallowing, clinical

and nutritional characteristics associated with weight loss >1% per week during

hospitalisation.


119

• Weight loss >1% per week during hospitalisation was also compared against the

clinical outcomes at univariate level. Linear regression was performed for

continuous outcome variables (LOS, 6-month MBI) with and without adjustment

for the covariates. Logistic regression was performed for dichotomous outcome

variables with and without adjustment for the covariates to identify any

associations between change in nutritional status and clinical outcomes. Odds

ratios, Β (unstandardised coefficient), 95% confidence interval and p values are

presented where applicable.



120

Table 2-9: List and types of variables for application in statistical analysis

Variables Independent Dependent Categorical Continuous Sociodemographic Age (years)/(60-75, 76-85, >85) � � � Gender (Male, Female) � � Race (Chinese, Non-Chinese#) � � Marital status (Single, Married, Divorced/Widowed)

� �

Pre-admission dwelling (Community, Institutionalised)

� �

Occupation (Retired, Working) � � Smoking status (Yes, No) � � Use of alcohol (Yes, No) � � Appetite, chewing and swallowing characteristics Dentition (Good, Poor) � � Dentures (Yes, No) � � Fitting of dentures (Well, Poor) � � Swallowing impairment (Yes, No) � � Modified-textured diet (Yes, No) � � Food consistency (Normal, Soft, Blended)

� �

Thickened fluid (Yes, No) � � Appetite (Good, Poor) � � Taste change (Yes, No) � � Clinical characteristics Dementia (Yes, No) � � Depression (Yes, No) � � Delirium (Yes, No) � � Number of prescribed drugs �� Pressure Ulcers (Yes, No) � � Hip fracture (Yes, No) � � Cancer (Yes, No) � � Abbreviated Mental Test (AMT) � � Charlson Comorbidity Index (CCI) � � Severity of Illness Index (SII) � � Serum albumin (ALB) � � � Total lymphocyte count (TLC) � � � Modified Barthel Index (MBI) (pre-morbid & admission)

� � �

Nutrition screening tools Tan Tock Seng Hospital Nutritional screening tool (TTSH NST) (At risk, Not at risk)

� � �

Mini Nutritional Assessment – Short Form (MNA-SF) (At risk, Not at risk)

� � �

Nutritional Risk Screening (NRS 2002) (At risk, Not at risk)

� � �

Short Nutritional Assessment Questionnaire (SNAQ©) (At risk, Not at risk)

� � �


121

Table 2-9: List and types of variables for application in statistical analysis (continued)

Variables Independent Dependent Categorical Continuous Nutrition assessment Weight � � Weight loss >1% per week̂ � � � Triceps Skinfold Thickness (TSF) � � Mid-arm circumference (MAC) � � Mid-arm muscle circumference (MAMC)*

� �

Mid-arm muscle area (MAMA)* � � Calf circumference (CC) � � Body mass index (BMI)*^ � � � � Corrected arm muscle area (CAMA)*^

� � � �

Subjective Global Assessment (SGA)^

� � �

Mini Nutritional Assessment (MNA)^

� � �

Clinical outcomes Length of stay (days)/(<11, >11) � � � Discharge higher level care (Yes, No)

� �

3-month readmission (Yes, No) � � 6-month mortality (Yes, No) � � 6-month MBI (score) � � * derived variables ^ depending on the analysis performed, the variables may be used as independent or dependent variables # Non-Chinese include: Malay, Indian, others. Variables in Bold were included as covariates in the multiple logistic regression analysis


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3 Recruitment and participants’ characteristics by

age

3.1 Introduction and aims Before the study examines the prevalence of malnutrition, the clinical outcomes, and

the implementation of appropriate nutrition assessment and screening approaches, it is

important to first understand the profile of older adults in an acute care hospital in

Singapore.

The aim of this chapter is to describe the recruitment process of participants in this

study and profile them using sociodemographic indicators, nutritional, appetite,

chewing, swallowing status, and clinical characteristics. Participants are stratified into

three age categories, 60-75 years, 76-85 years and > 85 years, for the purpose of

making comparisons. This chapter will answer the first research question, “What are

the nutritional, clinical, appetite, chewing and swallowing characteristics of older

adults (stratified by age) upon hospital admission?”

3.2 Recruitment

A total of 654 newly admitted patients under the Geriatric Medicine (GRM) in the

three specialist study wards were screened for eligibility (Figure 3-1). Thirty-nine per

cent (n=258) were excluded due to reasons listed in Table 3-1. Of the remaining 396

who fulfilled the inclusion criteria, 281 consented to the study and 115 did not

respond. This represented a recruitment success rate of 71% based on the 396 patients


123

who met the recruitment criteria. At the end of the 6-month follow-up, 278

participants completed the study and three (1%) discontinued participation.

The participants in this study had a similar profile to the GRM patients in the hospital

during the recruitment period (November 2006 to July 2007). Due to hospital bed

allocation policy, not all GRM patients are admitted directly to the three specialty

wards in TTSH. Some GRM are admitted to other non-specialty wards in the hospital,

whilst other patients may be admitted to a different discipline and subsequently

transferred under the care of the GRM team. Comparisons between all the GRM

patients in the hospital during the study period, the non-eligible patients from the

three study wards and the study participants are shown in Table 3-2. No significant

differences were revealed between the groups, except for age, where the non-eligible

patients were slightly younger. This is expected given that one of the eligibility

criterion was set at age >60 years old. There were eight patients who were younger

than 60 years old in the ineligible group.

In the other comparison, the study participants and non-respondents were not

significantly different from each other (Table 3-3).

Of the 281 participants who consented to the study, nutritional, appetite, chewing, and

swallowing for assessment were obtained directly from 134 participants (48%).

Families and caregivers provided information for 107 participants (38%) who were

unable to complete the interviews, whilst 37 participants (13%) required the

assistance of families/caregivers to complete the full assessment. Information was

obtained from the nursing homes for three (1%) participants.


124

Figure 3-1: Flowchart of sample recruitment from three specialist geriatric wards, consent and follow-up rate

Dropout by 6 months follow-up

Consent

Patients screened in the three geriatric wards on admission (November 2006-July 2007)

(n=654)

Yes (n=396) 61%

No (n=258) 39%

Yes (n=281) 71%

Non respondents (n=115)29%

Meet inclusion criteria

Yes (n=3) 1%

No (n=278) 99%


125

Table 3-1: Reasons for non-eligibility across the three study wards (n=258)

Aged <60 years 1 <1%

Palliative/terminally ill and age <60 7 3%

Palliative/terminally ill 77 30%

Dangerously ill list 47 18%

Admitted to hospital for >72 hours and still unable to obtain consent

62 24%

Readmission 51 20%

Discharged or deceased prior to recruitment

14 5%

Table 3-2: Comparison between all GRM patients, non-eligible patients from the three study wards and study participants during the study period (November 2006 to July 2007)

All GRM patients#

(n=1789)

Non-eligible patients from 3

study wards (n=258)

Study participants

(n=281)

p value

Age, years, mean +SD 80.0+9.0 79.5+8.9 81.3+7.6* 0.015†

Gender, n(%) Male Female

736(41) 1053(59)

135(52) 124(48)

124(44) 157(56)

0.077‡

Race, n(%) Chinese Malay Indian Other

1533(86)

82(5) 111(6) 63(3)

233(90) 13(5) 10(4) 3(1)

234(83) 22(8) 22(8) 3(1)

0.126‡

# Data based on all Geriatric Medicine (GRM) patients admitted to the hospital (including patients

from the non-participating wards) during the study period (November 2006 to July 2007)

†‡ Comparisons between non-eligible patients from the three study wards and study participants by

independent samples t-test (†) and Chi-square(‡)

* Significantly different between the non-eligible patients from the three study wards and study

participants, p<0.05


126

Table 3-3: Comparison between non-respondents and study participants

Non-respondents (n=115)

Study participants (n=281)

p value

Age, years, mean+SD 80.9+7.4 81.3+7.6 0.698†

Gender, n(%) Male Female

40(35) 75(65)

124(44) 157(56)

0.087‡

Race, n(%) Chinese Malay Indian Other

99(86) 9(8) 7(1) 0(0)

234(83) 22(8) 22(8) 3(1)

0.65‡

†‡ Comparisons between non-eligible patients and study participants by independent samples

t-test (†) and Chi-square(‡)


127

3.3 Participants’ (n= 281) characteristics

The sociodemographic characteristics of the participants stratified according to age

groups are presented in Table 3-4. The mean (+SD) age of the study participants was

81.3 (+7.6) years, with 82 participants (29%) >85 years. Chinese was the predominant

race (83%), 97% were community-dwellers, 99% were retirees, 97% were non-

smokers and 99% were non-drinkers. Marital status was significantly different

(p<0.05) between the age groups.

The nutritional characteristics of the participants stratified according to age groups are

presented in Table 3-5. A large proportion of the participants, 73% (n=204) were

unaware of their usual body weight. Upon admission, 85% (n=238) of participants

had their weight measured by the dietitian and nursing staff. One hundred and

seventy-five (62%) participants reported unquantifiable weight loss in the past six

months prior to hospital admission. Knee height measure was unobtainable in four

participants due contractures in the legs. Consequently, BMI and MNA were not

available for 45 (16%) participants. The mean (+SD) BMI was 21.1 (+4.0) kg/m2,

with 24%, 14% and 3% classified as underweight (<18.5kg/m2), overweight (25.0-

29.9 kg/m2) and obese (>30 kg/m2), respectively. The SGA was the only nutritional

assessment method that could be administered to all the participants. Admission

weight, estimated height from knee height, arm and calf measurements, and full MNA

score, were significantly different between the age groups (p<0.05). A decreasing

trend with the lowest measurements in the oldest age group (>85 years) was evident.


128

The appetite, chewing and swallowing characteristics of the participants stratified

according to age groups are presented in Table 3-6. Ninety per cent of participants

self-reported to have poor dentition. Forty-two per cent self-reported poor appetite

prior to hospitalisation. Sixty-four per cent self-reported having dentures, of which

29% were reportedly poor fitting. Ten per cent had swallowing impairment

documented in their medical records. Forty-one per cent self-reported to be on a

modified texture diet. None of these characteristics was significantly different

between the age groups.

The participants were admitted to the GRM wards with the five most frequent

diagnoses: cerebrovascular disorders (19%), urinary tract infection (19%), respiratory

infection (10%), muscular-skeletal problems/fractures (8%), and gastrointestinal

problems (6%). The clinical characteristics of the participants stratified according to

age groups are presented in Table 3-7. The mean (+SD) number of prescribed drugs

was 4.6 (+3.0), mean (+SD) Abbreviated Mental Test (AMT) score was 5.7 (+3.3),

mean (+SD) Modified Barthel Index (MBI) score on admission was 59.3 (+28.1) and

33% participants had been diagnosed with dementia. AMT score, serum albumin, and

Charlson Comorbidity Index (CCI) were significantly different (p<0.05) between the

age groups.

The clinical outcomes of the participants stratified according to age groups are

presented in Table 3-8. The median LOS was nine days (range 2-50 days) and 44%

participants had LOS >11days. Twenty-nine per cent were discharged to higher level

care. Readmission rate was 35% at 3-months, and. mortality at 6-months was 11%.

Mean (+SD) MBI at 6-months was 70.5 (+31.9). MBI at 6-months was the only


129

clinical outcome significantly different (p<0.05), between the age groups with the

lowest MBI shown among those aged >85 years.


130

Table 3-4: Sociodemographic characteristics of 281 hospitalised older adults (>60 years) and according to age groups

Demographics Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) p value‡ Age, years 81.3+7.6 70.9+3.7 80.7+2.9 90.0+3.8 - Gender Male Female

124(44) 157(56)

33(53) 29(47)

59(43) 78(57)

32(39) 50(61)

0.222

Race Chinese Malay Indian Others

234(83) 22(8) 22(8) 3(1)

48(77) 8(13) 4(7) 2(3)

113(82) 11(8) 12(9) 1(1)

73(89) 3(4) 6(7) 0(0)

0.203#

Marital status Single Married Divorced/widowed

22(8) 97(34) 162(58)

10(16) 33(53) 19(31)

7(5)

44(32) 86(63)

5(6)* 20(24) 57(70)

<0.001

Preadmission dwelling Community Institutionalised

272(97)

9(3)

61(98) 1(2)

133(97)

4(3)

78(95) 4(5)

0.526#

Occupation Retired Working

277(99)

4(1)

59(95) 3(5)

137(100)

0(0)

81(99) 1(1)

0.028#

Smoking Yes No

8(3)

273(97)

4(6)

58(94)

3(2)

134(98)

1(1)

81(99)

0.141#

Alcohol Yes No

3(1)

278(99)

3(5)

59(95)

0(0)

137(100)

0(0)

82(100)

0.005#

Results in table are expressed as n(%), except for age as mean+SD. ‡Comparisons between age groups performed by Chi-squared test; # More than 50% of cell counts less than 5, Chi-squared test may not be valid * Significantly different between the age groups, p<0.05


131

Table 3-5: Nutritional characteristics of 281 hospitalised older adults (>60 years) and according to age groups

Anthropometrics n Total (n=281) 60- 75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) F value p value† Weight, height, BMI Usual weight (kg)a 77 54.3+10.2(30.1-76.0) 58.7+8.7(40-76) 53.6+10.6(35-76) 50.9+9.6(30.1-67) 2.78 0.069

Admission weight (kg) 238 49.6+10.3(26.4-79.9) 53.2+10.7(30.4-79.9) 49.7+9.9(30.3-74.3) 46.4+9.6(26.4-70.8)* 7.10 0.001

Weight loss prior to admissionb Yes No Unsure

281 175(62) 102(36)

4(1)

39(63) 23(37)

-

82(60) 54(39) 1(1)

54(66) 25(30) 3(4)

-

0.226‡

Knee height (cm) 277 46.8+3.1(39.5-54.7) 47.5+3.0(39.6-53.5) 46.8+3.0(39.5-54) 46.4+3.2(40.7-54.7) 2.09 0.125

Estimated height from knee height (cm)c

277 153.3+9.0 (133.3-173.7)

155.8+8.2 (134.5-171)

153.2+8.8 (133.3-172.1)

151.5+9.6** (135.9-173.7)

4.09 0.018

BMI (kg/m 2)d Underweight, <18.5 kg/m2 Normal, 18.5-24.9 kg/m2 Overweight, 25-29.9 kg/m2 Obese, >/=30 kg/m2

236

21.1+4.0(12.2-32.9) 57(24) 139(59) 33(14) 7(3)

21.9+3.9(14.1-32.5) 12(22) 31(56) 10(18) 2(4)

21.3+4.1(12.2-32.9) 24(21) 69(61) 16(14) 4(4)

20.3+3.9(12.3-31.6) 21(31) 39(57) 7(10) 1(2)

2.72 0.068 0.670‡

Arm and calf measurements MAC (cm) 279 24.6+4.0(14.0-37.5) 25.8+4.6(14.0-37.5) 24.9+3.9(17.4-36.5) 23.2+3.5(14.3-31.0)** 8.34 <0.001

TSF (mm) 278 11.0 (3.0-37.0) 12.0 (4.0-37.0) 12.0(4.0-36.0) 10.0(3.0-23.0)*** - 0.011§

MAMC (cm) 278 20.8+2.8(12.6-27.3) 21.5+3.0(12.6-27.3) 21.0+2.6(15.5-27.1) 20.0+2.7(12.7-26.8)** 6.06 0.003

MAMA (cm 2) 278 34.6+9.0(12.4-58.4) 37.1+9.7(12.4-58.4) 35.1+8.7(18.9-57.6) 32.0+8.4(12.7-56.2)** 6.24 0.003

CAMA (cm 2) >21.6cm2(male);>21.4 (female) <21.6cm2(male);<21.4 (female)

278 26.6+8.9(5.9-51.1) 199(72) 79(28)

28.8+9.5(5.9-50.8) 48(79) 13(21)

27.1+8.7(8.9-51.1) 100(74) 35(26)

24.1+8.3(6.2-49.7)** 51(62) 31(38)

5.39 0.005 0.065‡

CC (cm) 278 29.2+3.7(18.5-37.5) 30.6+3.7(18.8-37.5) 29.5+3.6(18.5-36.3) 27.6+3.4(19.0-36.0)** 13.08 <0.001


132

Table 3-5: Nutritional characteristics of 281 hospitalised older adults (>60 years) and according to age groups (continued)

Composite nutrition assessment

n Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) F value p value†

SGA

Well-nourished, A Moderately malnourished, B Severely malnourished, C

281

184(65) 80(29) 17(6)

44(71) 14(23) 4(6)

91(66) 40(29) 6(5)

49(60) 26(32) 7(8)

0.512‡

Full MNA score 236 19.8+5.0(7.0-28.5) 21.2+4.9(7.5-28.0) 19.9+4.8(7.0-28.5) 18.5+5.2(7.5-28.5)* 4.47 0.012 MNA

Well-nourished, >23.5 At risk, 17-23.5 Malnourished, <17

236

56(24) 126(53) 54(23)

20(36) 25(46) 10(18)

25(22) 64(57) 24(21)

11(16) 37(54) 20(30)

0.082‡

Abbreviations: BMI: body mass index; MAC: mid-arm circumference; TSF: triceps skinfold thickness; MAMC: mid-arm muscle circumference; MAMA: mid-arm muscle area; CAMA: corrected arm muscle area; CC: calf circumference; SGA: Subjective Global Assessment; MNA: MiniNutritional Assessment Results are expressed as mean+SD(range) for all anthropometric measurements and full MNA score and as n(%) for SGA and MNA nutrition status. TSF expressed as median (range) as it was a non-parametric variable. †‡§Comparisons between age groups performed by ANOVA (†), Chi-squared test (‡) and Kruskal Wallis Test (§) * Significantly different between age groups >85yrs and 60-75 yrs based on post-hoc Bonferroni test. ** Significantly different between groups >85yrs and 60-75 yrs, and groups >85 yrs and 76-85 yrs, based on post-hoc Bonferroni test ***Significantly different between the age groups, p<0.05. No post-hoc test for Kruskal Wallis Test. a Self-reported by participants or caregivers. Most participants were unaware of their usual weight resulting in a low response rate (n=77). b Weight loss 6-month prior admission as self-reported by participants or caregivers. 98% of those who reported weight loss were unable to quantify the actual amount of loss. c Height estimated from knee height using predictive equations: Men: 51.16 + (2.24 x knee height) and Women: 46.11 + (2.46 x knee height) – (0.12 x age)253 d Calculated from estimated height from knee height. Only 236 out of 281 participants were clinically safe to have both weight and knee height measured on admission


133

Table 3-6: Appetite, chewing and swallowing characteristics of 281 hospitalised older adults (>60 years) and according to age groups

Appetite, chewing and swallowing n Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) p value‡ Âppetite Good Poor

274# 159(58) 115(42)

34(55) 28(45)

82(62) 50(38)

43(54) 37(46)

0.744

^Taste change Yes No

274# 10(4)

264(96)

2(3)

60(97)

5(4)

127(96)

3(4)

77(96)

0.980

^Dentition Good Poor

281 29(10) 252(90)

7(11) 55(89)

15(11) 122(89)

7(8)

75(92)

0.817

^Dentures Yes No

281 180(64) 101(36)

39(63) 23(37)

86(63) 51(37)

55(67) 27(33)

0.795

^Fitting of dentures Well Poor

180 127(71) 53(29)

30(77) 9(23)

58(67) 28(33)

39(71) 16(29)

0.558

Swallowing impairment Yes No

281 29(10) 252(90)

5(8)

57(92)

12(9)

125(91)

12(15) 70(85)

0.309

^Modified texture diet Yes No

274# 113(41) 161(59)

19(31) 43(69)

55(42) 77(58)

39(49) 41(51)

0.093

^Thickened fluid Yes No

274# 9(3)

265(97)

3(5)

59(95)

3(2)

129(98)

3(4)

77(96)

0.621*

Results in table are expressed as n(%). ^Self-reported by participants or caregivers. # Participants on nasogastric feedings (n=7) were excluded. ‡Comparisons between age groups performed by Chi-squared test. * More than 50% of cell counts less than 5, Chi-squared test may not be valid


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Table 3-7: Clinical characteristics of 281 hospitalised older adults (>60 years) and according to age groups

Clinical n Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) F value p value‡ aDementia Yes No

281 93(33) 188(67)

17(27) 45(73)

44(32) 93(68)

33(40) 49(60)

- 0.244

bDepression Yes No

268 70(26) 198(74)

15(25) 44(75)

41(31) 91(69)

14(18) 63(82)

- 0.123

Delirium Yes No

279 31(11) 248(89)

6(10) 56(90)

18(13) 118(87)

7(9)

74(91)

- 0.535

Total number prescribed drugs

281 4.6+3.0(0-12) 4.3+3.5(0-12) 4.7+2.8(0-12) 4.5+2.9(0-12) 0.45 0.640†

cAMT score

257 5.7+3.3(0-10) 6.6+3.2 (0-10) 5.7+3.2 (0-10) 5.1+3.3(0-10)* 3.73 0.025†

dSeverity of Illness score

280 2.1+0.4(1-4) 2.1+0.4(1-4) 2.1+0.4(1-4) 2.2+0.4(2-3) 1.79 0.168†

eCharlson Comorbidity Index score

280 5.5+1.4(2-12) 4.6+1.5(2-10) 5.4+1.2(3-12) 6.3+1.2(3-10)** 30.61 <0.001†

Functional fPre-morbid MBI

281 80.2+28.7(0-100) 83.6+27.0(0-100) 81.2+29.2(0-100) 76.0+29.2(0-100) 1.38 0.253†

Admission MBI

281 59.3+28.1(0-100) 65.0+26.8(0-100) 59.2+28.1(0-100) 55.1+28.7(0-100) 2.22 0.111†


135

Table 3-7: Clinical characteristics of 281 hospitalised older adults (>60 years) and according to age groups (continued)

Biochemical n Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137) >85 yrs (n=82) F value p value‡ Serum albumin (mg/L) >= 35mg/L < 35mg/L

276 32.3+5.2(15-43) 104(38) 172(62)

33.7+5.9(17-43) 32(53) 28(47)

32.5+4.8(15-42) 49(36) 86(64)

31.1+5.2(15-40)* 23(28)* 58(72)

4.57 0.011† 0.009

TLC (x103/mm3) >= 1.5x103/mm3 < 1.5x103/mm3

281 1.2 (0.1-4.0) 107(38) 174(62)

1.3 (0.1-3.7) 25(40) 37(60)

1.3 (0.3-3.6) 59(43) 78(57)

1.1 (0.3-4.0) 23(28) 59(72)

- 0.196§ 0.079

Abbreviations: AMT: abbreviated mental test; TLC: total lymphocyte count; MBI: Modified Barthel Index Results are expressed as n(%) or mean+SD(range). TLC expressed as median (range) as it was a non-parametric variable. †‡§Comparisons between age groups performed by ANOVA (†), Chi-squared test(‡) and Kruskal Wallis Test (§) * Significantly different between age groups >85yrs and 60-75 yrs based on post-hoc Bonferroni test. ** Significantly different between groups >85yrs and 60-75 yrs, and groups >85 yrs and 76-85 yrs, based on post-hoc Bonferroni test a Defined using DSM IV criteria354 by physician b Defined using a single question “Do you often feel sad or depressed?”355 c Abbreviated Mental Test356 – 10-item test with maximum of 10 points. Score <7 indicates cognitive impairment368 d Modified from original severity of illness index358 into 4 level (of increasing severity from 1 to 4) burden of illness measure. e Charlson comorbidity index357 takes into account the number and the seriousness of comorbid diseases. Higher scores indicate a more severe

condition and prognosis. f Modified Barthel Index162 evaluates the level of assistance required for activities of daily living. Scoring is based on a continuous scale 0-100,

with 100 indicating independent function.


136

Table 3-8: Clinical outcomes of 281 hospitalised older adults (>60 years) and according to age groups

Outcomes

n Total (n=281) 60-75 yrs (n=62) 76-85 yrs (n=137)

>85 yrs (n=82) F value p value‡

LOS, days >/= 11 days <11 days

280 9(2-50) 123(44) 157(56)

9(2-32) 28(46) 33(54)

9(3-42) 57(42) 80(58)

9(2-50) 38(46) 44(54)

0.09 0.759§

0.744

Discharge higher level care Yes No

277 81(29) 196(71)

21(34) 40(66)

37(27) 99(73)

23(29) 57(71)

- 0.584

3-month Readmission Yes No

261 91(35) 170(65)

14(24) 44(76)

49(38) 81(62)

28(38) 45(62)

- 0.150

6-month Mortality Yes No

279 32(11) 247(89)

5(8)

56(92)

14(10) 122(90)

13(16) 69(84)

- 0.304

6-month MBI 246 70.5+31.9 80.1+26.3

69.7+33.0

64.1+32.5*

4.10 0.018†

Abbreviations: LOS: length of stay; MBI: Modified Barthel Index Results in table are expressed as n(%), median (range) for LOS, or mean+SD for 6-month MBI. †‡Comparisons between age groups performed by ANOVA (†), Chi-squared test(‡) and Kruskal Wallis Test (§) * Significantly different between age groups >85yrs and 60-75 yrs based on post-hoc Bonferroni test.


137

3.4 Discussion

3.4.1 Recruitment

The study recruitment rate was relatively high (71%) and the participant retention rate

was extremely good at 99%. The participants’ characteristics reflected were similar to

that of the study population. All these indicate that the study sample was a close

representation of the patients admitted to the GRM wards at TTSH.

A large proportion (39%) of the sample screened was ineligible, with up to half being

terminally ill (30%) or dangerously ill (18%; Table 3-1). The participants who were

excluded from the study due to their medical conditions may have higher malnutrition

risk369. Therefore the recruited sample might have underestimated the prevalence of

malnutrition and its impact on adverse outcomes in older adults from acute care.

Another 24% screened were admitted for more than 72 hours and hence considered

ineligible. These included GRM patients who were transferred into the study wards

from the non-study wards in TTSH, and those whom families could not be contacted

for consent within the stipulated timeframe.

3.4.2 Participants’ characteristics

Fewer than half the participants (48%) could provide information independently for

all aspects of their assessment. This indicated that the reliance on proxies

(families/caregivers) was crucial to complete the assessments. Although only a small

proportion (10%) of the participants had documented swallowing impairment, 40% of

participants self-reported being on modified consistency diets. This could be partly

attributed to poor dentition reported by a large proportion of participants (90%). Their


138

poor oral health could directly affect the adequacy of their oral intake with modified

consistency diets47, 370, 371. In addition, 42% reported to have poor appetite and 33%

were cognitively impaired, which could further impact on their oral intake. Up to two-

thirds of the participants reported weight loss 6-month prior to admission, and most

participants had multiple comorbidities. All these findings highlighted that a large

proportion of participants had characteristics associated with high malnutrition risk.

The oldest age group (>85 years), which made up 29% of the study population, were

clinically and nutritionally different from the youngest (60-75 years) age group. The

oldest group was significantly poorer in nutritional characteristics, particularly

anthropometric measurements (Table 3-5). They also had a higher number of and

more serious comorbid diseases, were more cognitively impaired, (Table 3-7) and had

poorer functional status 6-months post-discharge (Table 3-8). These characteristics

suggested that the oldest old were exposed to greater malnutrition risks compared to

the youngest old in this study and they should be given more attention and receive

closer monitoring.


139

3.5 Conclusion

The sample from this study consisted of hospitalised older adults who are

representative of the GRM patients in TTSH. These participants presented with a high

prevalence of established risk factors for malnutrition (as discussed in section 1.2).

These risks were even more prevalent with increased age, especially amongst the

oldest old (>85 years). Their poorer nutritional and clinical characteristics made them

more vulnerable to and predisposed them to higher risks of developing malnutrition

prior to and during hospitalisation. Therefore upon acute hospital admission, they

would most likely benefit from closer assessment and monitoring of their nutritional

status.

The prevalence of malnutrition is described in more detail, and the extent to which the

risk factors (participants’ characteristics) were associated with malnutrition is

presented and discussed in the next chapter.


140

4 Prevalence of malnutrition and comparison of

participants’ characteristics by nutritional status

4.1 Introduction and aims

Older hospitalised patients are more likely to experience poor nutritional status

(discussed in section 1.4) and this has been widely reported in Western countries.

However it has not been as well-studied in Asia and Singapore (discussed in section

1.5). It is therefore important to establish the malnutrition prevalence in Singapore

acute care settings and to characterise malnourished patients so appropriate

malnutrition prevention and intervention programs can be planned and implemented

to effectively manage it.

The aim of this chapter is to describe the prevalence of malnutrition, defined by SGA,

MNA, BMI and CAMA, of older adults on admission to a Singapore acute hospital

and their associated characteristics. Participant sociodemographic, nutritional, appetite,

chewing, swallowing and clinical characteristics are compared by classifying them as

well-nourished or malnourished. The objective is to identify the factors associated

with malnutrition on admission. This chapter will answer the second research question,

“What is the prevalence of malnutrition, as defined SGA, MNA, BMI, CAMA, of

older adults upon hospital admission and its associated characteristics?”

4.2 Methods

Four different nutritional assessment tools and indices, SGA, MNA, BMI, CAMA

(discussed in Chapter 2 section 2.2.2), were used to define malnutrition in this study.


141

The SGA categories B and C were combined to form the “malnourished” category in

this study, as the frequency for SGA category C (17/281; 6%) was relatively low.

MNA classification for nutritional status was also dichotomised (well-nourished/at

risk and malnourished) as a clear distinction needed to be made between well-

nourished and malnourished participants. This was previously discussed in Chapter 2

(section 2.2.2.2).

The prevalence of malnutrition using the four identified assessment methods was

described and kappa estimates were derived to evaluate the agreement of malnutrition

assessment between them. Participants with and without admission weight

measurements were analysed to identify any differences in their characteristics and

malnutrition prevalence. To identify the characteristics associated with malnutrition,

the well-nourished and malnourished participants (across all four methods of

malnutrition assessment) were compared using independent samples t-test, Chi-

squared test and Mann-Whitney test.


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4.3 Results

4.3.1 Prevalence of malnutrition on admission

The four nutritional assessment tools and indices yielded a range (23% to 35%) of

malnutrition prevalence (Figure 4-1). SGA detected the highest prevalence (35%) of

malnutrition, while MNA detected the lowest prevalence (23%). The completion rate

for MNA and BMI was 84% (n=236) due to unavailability of admission weight

(n=43) and/or knee height (n=3) measurements. CAMA and SGA were completed for

99% and 100% of participants, respectively. Prevalence of malnutrition was higher

among participants aged >85 years (Table 3-5: 30-40% across all assessment

methods) than those aged <75 years (18-29% across all assessment methods).

The lack of admission weight was the main reason for incomplete MNA and BMI

(n=43/45). Table 4-1 compares the characteristics of the participants with and without

admission weight measurements to identify any differences in those excluded from

the MNA and BMI analysis. Participants without admission weight had significantly

higher mean Severity of Illness Index scores (SII; 2.2+0.5 vs 2.1+0.3, p<0.05), lower

mean admission MBI (31.5+27.7 vs 64.3+25.2, p<0.05) and were more malnourished

(defined by SGA, 51% vs 32%, p<0.05). They were not significantly different in any

of the sociodemographic, cognitive and comorbidity characteristics.

The SGA and MNA had moderately strong agreement (84%, kappa estimates 0.60;

Table 4-2)372. The agreement between the malnutrition assessment methods were

fair372 (74-82%) as shown by the kappa estimates (0.35-0.52, p<0.001; Table 4-2).


143

Figure 4-1: Prevalence of malnutrition among older adults (>60 years) on

admission to specialist geriatric wards using different malnutrition assessment

methods

35

23 24

28

0

5

10

15

20

25

30

35

Mal

nour

ishe

d, %

SGA (n=281)MNA (n=236)BMI (n=236)CAMA (n=278)

Abbreviations: SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment;

BMI: body mass index; CAMA: corrected arm muscle area

Assessment methods of malnutrition:

SGA*: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B

(moderately malnourished) and C (severely malnourished) 235;

MNA* : Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA

score<17 (malnourished) 339;

BMI : Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259;

CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female),

Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286

* Refer to Table 3-5 for prevalence of each SGA rating A, B and C, and each MNA

categories of well-nourished, at risk and malnourished.

Note: Only 236 out of 281 participants were clinically fit to have both their weight and knee

height measurements taken on admission


144

Table 4-1 : Comparison of participants’ characteristics and malnutrition prevalence between those with and without weight measurements on admission

Admission weight

available

(n=238)

Admission weight

not available

(n=43)

P value‡

Age 81.0+7.7 82.9+6.6 0.118†

Gender-Male

-Female

104(44)

134(56)

20(47)

23(53)

0.732

Race-Chinese

-Non-Chinese

197(83)

41(17)

37(86)

6(14)

0.597

Dementia -Yes

- No

76(32)

162(68)

18(42)

25(58)

0.204

Depression-Yes

- No

61(27)

167(73)

9(22)

31(78)

0.572

Number of

prescribed drugs

4.6+2.9 4.5+3.2 0.871†

Severity of Illness a 2.1+0.3 2.2+0.5 0.016†

Charlson

Comorbidity Index b

5.5+1.4 5.6+1.1 0.573†

Admission MBI c 64.3+25.2 31.5+27.7 <0.001†

SGA

Well-nourished

Malnourished

163(68)

75(32)

21(49)

22(51)

0.013

CAMA

Well-nourished

Malnourished

174(74)

62(26)

25(60)

17(40)

0.060

Abbreviations: SGA: Subjective Global Assessment; CAMA: corrected arm muscle area; MBI; Modified Barthel Index Results in table are expressed as n(% of total) or mean+SD. †‡Comparison between participants with and without admission weight was performed using independent samples t-test(†) and Chi-squared test(‡). SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female), Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286

a Modified from original Severity of Illness Index358 into 4 level (of increasing severity from 1 to 4) burden of illness measure.

b Charlson Comorbidity Index357 takes into account the number and the seriousness of comorbid diseases. Higher scores indicate a more severe condition and prognosis.

c Modified Barthel Index162 evaluates the level of assistance required for activities of daily living. Scoring is based on a continuous scale 0-100, with 100 indicating independent function.


145

Table 4-2: Classification and agreement of the four different malnutrition assessment methods applied to hospitalised older adults (>60 years)

MNA (n=236) BMI (n=236) CAMA (n=278)

n Well-nourished (n=182)

Malnourished (n=54)

Well-nourished (n=179)

Malnourished (n=57)


Malnourished (n=79)

SGA Well-nourished Malnourished Agreement (%) Kappa p value

281 153(65) 29(12)

9(4)* 45(19)

84 0.596 <0.001

146(62) 33(14)

16(7)* 41(17)

79 0.486

<0.001

154(55) 45(16)

28(10)* 51(18)

74 0.394 <0.001

MNA Well-nourished Malnourished Agreement (%) Kappa p value

236 153(65) 26(11)

29(12) * 28(12)

77 0.352

<0.001

150(64) 24(10)

32(14)* 30(13)

76 0.361

<0.001 BMI Well-nourished Malnourished Agreement (%) Kappa p value

236

155(66) 19(8)

24(10)* 38(16)

82 0.517

<0.001 Abbreviations: BMI: body mass index; CAMA: corrected arm muscle area; SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment Results are expressed as n(% of total). Measure of agreement using kappa estimates * Significantly good agreement between the two malnutrition assessment methods, p<0.05 SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; CAMA: Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286


146

4.3.2 Comparison of participants’ characteristics b y nutritional

status on admission

The sociodemographic characteristics of participants stratified by nutritional status are

presented in Table 4-3. There were no significant differences (p>0.05) in

sociodemographic characteristics between the well-nourished and malnourished

participants, except that CAMA-defined malnourished participants were significantly

older (82.8+7.2 vs 80.7+7.7 years, p<0.05) and constituted more Chinese (92% vs

80%, p<0.05). Significantly more (p<0.05) malnourished participants defined by BMI

were institutionalised prior to hospital admission (9% vs 2%).

The appetite, chewing and swallowing characteristics of participants were shown to

be significantly (p<0.05) associated with nutritional status (Table 4-4). A significantly

higher proportion of malnourished participants (regardless of malnutrition assessment

methods) had swallowing impairment (16-26% vs 3-6%), poor appetite (53-77% vs

24-38%) and were on a modified texture diet (49-59% vs 33-37%) as compared to the

well-nourished participants. All malnourished groups, except the BMI-defined group,

had a significantly higher number of participants with poor-fitting dentures (33-43%

vs 22-25%). BMI-defined malnourished participants had poorer dentition (98% vs

88%), whilst more malnourished participants defined by MNA had taste changes

(10% vs 2%) and were on thickened fluid (10% vs 2%).

This study showed that regardless of the assessment methods used to determine

nutritional status, there were significant (p<0.05) differences in clinical characteristics

between the well-nourished and malnourished groups (Table 4-5). Pre-morbid MBI,

admission MBI, AMT score and serum albumin were significantly lower (p<0.05)


147

among the malnourished groups across all assessment methods of malnutrition. The

malnourished patients were more likely to have dementia (44-56% vs 24-29%). All

malnourished groups, except the BMI-defined group, had significantly higher

Charlson Comorbidity Index (CCI) scores. Depression was significantly higher in the

SGA- and MNA-defined malnourished groups. TLC was significantly lower in the

BMI- and CAMA-defined malnourished groups.

Participants’ characteristics found to be significantly associated with malnutrition are

summarised in Table 4-6. There were eight characteristics which were significantly

associated with all the assessment methods of malnutrition. These included those who

reported to have poor appetite, to be on a modified texture diet, presented with

swallowing and cognitive impairment, low serum albumin, poor functional status, and

who suffered from dementia or depression. MNA- and CAMA-defined malnutrition

was shown to be significantly associated with the most number (n=13) of participant

characteristics compared to BMI (n=12), and SGA (n=11).


148

Table 4-3: Sociodemographic characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission

SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278)

Demographics Well-nourished (n=184)

Malnourished (n=97)


Malnourished (n=54)


Malnourished (n=57)


Malnourished (n=79)

Mean Age, years p value†

81.0+7.7 (61-102)

81.9+7.4 (64-102)

0.351

80.7+7.8 (61-100)

82.1+7.6 (64-102)

0.242

80.7+7.7 (61-100)

81.9+7.9 (65-102)

0.313

80.7+7.7 (61-102)

82.8+7.2* (65-102)

0.045 Gender Male Female p value‡

77(42) 107(58)

47(48) 50(52) 0.289

81(45) 101(55)

21(39) 33(61) 0.464

73(41) 106(59)

29(51) 28(49) 0.180

90(45) 109(55)

31(39) 48(61) 0.364

Race Chinese Non-Chinese (Malay, Indian, Other) p value‡

153(83) 31(17)

81(84) 16(16)

1.000

155(85) 27(15)

42(78) 12(22)

0.199

151(84) 28(16)

46(81) 11(19)

0.518

160(80) 39(20)

73(92) 6(8)

0.014

Marital status Single Married Divorced/Widowed p value ‡

13(7) 64(35) 107(58)

9(9)

33(34) 55(57) 0.740

16(9) 66(36) 100(55)

6(11) 16(30) 32(59) 0.636

16(9) 63(35) 100(56)

6(11) 19(33) 32(56) 0.923

13(7) 72(36) 114(57)

9(11) 22(28) 48(61) 0.227

Social history Community Institutionalised p value ‡ ̂

181(98)

3(2)

91(94) 6(6)

0.068

178(98)

4(2)

50(93) 4(7)

0.083

176(98)

3(2)

52(91)*

5(9) 0.022

194(97)

5(3)

75(95) 4(5)

0.279


149

Table 4-3: Sociodemographic characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission (continued)

SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278)

Demographics Well-nourished (n=184)

Malnourished (n=97)


Malnourished (n=54)


Malnourished (n=57)


Malnourished (n=79)

Occupation Retired Working p value ‡ ̂

180(98)

4(2)

97(100)

0(0) 0.302

179(98)

4(2)

54(100)

0(0) 0.576

175(98)

4(2)

57(100)

0(0) 0.575

195(98)

4(2)

79(100)

0(0) 0.580

Smoking: Yes No p value ‡ ̂

6(3) 178(97)

2(2) 95(98) 0.719

7(4) 175(96)

1(2) 53(98) 0.686

6(3) 173(97)

2(4) 55(96) 1.000

7(4) 192(96)

1(1) 78(99) 0.447

Alcohol: Yes No p value ‡ ̂

2(1) 182(99)

1(1) 96(99) 1.000

2(1) 180(99)

1(2) 53(98) 0.543

2(1) 177(99)

1(2) 56(98) 0.565

2(1) 197(99)

1(1) 78(99) 1.000

Abbreviations: SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment; BMI: body mass index; CAMA: corrected arm muscle area †‡^ Comparisons between well-nourished and malnourished participants within each malnutrition assessment method performed by independent samples t-test (†) and Chi-squared test(‡). Fisher exact test was presented when expected count for at least one cell was less than 5(^). * Significantly different between the well-nourished and malnourished participants for each malnutrition assessment method, p<0.05 SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286


150

Table 4-4: Appetite, chewing and swallowing characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission

n SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278)

Appetite, chewing and swallowing


Malnourished (n=97)


Malnourished (n=54)


Malnourished (n=57)


Malnourished (n=79)

Âppetite Good Poor p value ‡

274# 137(76) 43(24)

22(23)* 72(77) <0.001

121(67) 60(33)

17(33)* 35(67) <0.001

116(65) 62(35)

22(40)* 33(60) 0.001

122(62) 74(38)

35(47)* 40(53) 0.016

^Taste Change Yes No p value ‡ ̂

274# 4(2)

176(98)

6(6)

88(94) 0.097

4(2)

117(98)

5(10)* 47(90) 0.028

7(4)

171(96)

2(4)

53(96) 1.000

5(3)

191(97)

5(7)

70(93) 0.146

^Dentition Good Poor p value ‡ ̂

281 22(12) 162(88)

7(7)

90(93) 0.214

20(11) 162(89)

2(4)

52(96) 0.106

21(12) 158(88)

1(2)* 56(98) 0.024

24(12) 175(88)

4(5)

75(95) 0.080

^Dentures Yes No p value ‡

281 115(62) 69(38)

65(67) 32(33) 0.454

117(64) 65(36)

35(65) 19(35) 0.943

114(64) 65(36)

38(67) 19(33) 0.682

126(63) 73(37)

52(66) 27(34) 0.695

^Fitting of dentures Well Poor p value ‡

180 89(77) 26(23)

38(58)* 27(42) 0.007

91(78) 26(22)

20(57)* 15(43) 0.016

86(75) 28(25)

25(66) 13(33) 0.246

94(75) 32(25)

31(60)* 21(40) 0.047

Swallowing Impairment Yes No p value ‡

281 10(5)

174(95)

19(20)* 78(80) <0.001

6(3)

176(97)

14(26)* 40(74) <0.001

11(6)

168(94)

9(16)* 48(84) 0.030

12(6)

187(94)

16(20)* 63(80) <0.001


151

Table 4-4: Appetite, chewing and swallowing characteristics [n(%)] of 281 older adults aged >60 years according to nutritional status on admission (continued)

n SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278)

Appetite, chewing and swallowing


Malnourished (n=97)


Malnourished (n=54)


Malnourished (n=57)


Malnourished (n=79)

^Modified Texture Diet Yes No p value ‡

274# 61(34) 119(66)

52(55)* 42(45) 0.001

63(35) 118(65)

32(62)* 20(38) 0.001

65(37) 113(63)

30(55)* 25(45) 0.019

73(37) 123(63)

39(52)* 36(48) 0.038

^Thickened fluid Yes No p value ‡ ̂

274# 4(2)

176(98)

5(5)

89(95) 0.282

3(2)

178(98)

5(10)* 47(90) 0.015

5(3)

173(97)

3(5)

52(95) 0.346

4(2)

192(98)

4(5)

71(95) 0.152

Abbreviations: SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment; BMI: body mass index; CAMA: corrected arm muscle area ^Self-reported by participants or caregivers. # Participants on nasogastric feedings (n=7) were excluded. ‡^ Comparisons between well-nourished and malnourished participants within each nutrition diagnostic criterion performed by Chi-square(‡).Fisher exact test was presented when expected count for at least one cell was less than 5(^). * Significantly different between the well-nourished and malnourished participants within the nutrition diagnostic criterion, p<0.05 SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286


152

Table 4-5: Clinical characteristics of 281 older adults aged >60 years according to nutritional status on admission

n SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278) Clinical Well-nourished

(n=184) Malnourished

(n=97) Well-nourished






(n=79) aDementia: Yes No p value ‡

281 51(28) 133(72)

43(44)* 54(56) 0.005

46(25) 136(75)

29(54)* 25(46) <0.001

43(24) 136(76)

32(56)* 25(44) <0.001

58(29) 141(71)

35(44)* 44(56) 0.016

bDepression: Yes No p value ‡

268 39(22) 137(78)

31(34)* 61(66) 0.041

40(23) 136(77)

20(40)* 30(60) 0.015

43(25) 130(75)

17(32) 36(68) 0.298

49(26) 142(74)

20(27) 54(73) 0.819

Delirium: Yes No p value ‡

279 18(10) 166(90)

13(14) 82(86) 0.326

13(7) 169(93)

5(9) 48(91) 0.564

12(7) 166(93)

6(11) 51(89) 0.392

18(9) 179(91)

12(15) 67(85) 0.144

Total number prescribed drugs p value †

281 4.6+3.1(0-12) 4.6+2.7(0-10) 0.924

4.4+3.0(0-12) 5.2+2.6(0-11) 0.080

4.7+2.9(0-12) 4.3+2.9(0-11) 0.387

4.6+3.0(0-12) 4.4+3.0(0-11) 0.714

cAMT score p value †

257 6.2+3.1 (0-10)

4.9+3.4* (0-10) 0.002

6.3+3.0 (0-10)

4.4+3.6* (0-10) <0.001

6.2+3.1 (0-10)

4.7+3.3* (0-10) 0.003

6.2+3.1 (0-10)

4.5+3.3* (0-10) <0.001

dSeverity of Illness Index Score p value †

280 2.1+0.4 (1-4)

2.1+0.3 (1-3) 0.640

2.1+0.3 (1-4)

2.1+0.4 (1-3) 0.850

2.1+0.4 (1-4)

2.1+0.3 (1-3) 0.941

2.1+0.4 (1-4)

2.1+0.4 (1-4) 0.866

eCharlson Comorbidity Index p value †

280 5.3+1.4(3-12) 5.7+1.3*(2-10) 0.028

5.2+1.3(2-10) 6.3+1.4*(4-12) <0.001

5.4+1.5(2-12) 5.6+1.3(3-9) 0.390

5.3+1.3(2-9) 5.9+1.6*(3-12) 0.001

Functional fPre-morbid MBI p value †

281 86.5+23.5 (0-100)

68.4+33.8* (0-100) <0.001

90.2+18.3 (0-100)

65.9+31.7* (0-100) <0.001

88.3+19.3 (0-100)

73.2+33.0* (0-100) <0.001

86.3+22.7 (0-100)

65.8+26.1* (0-100) <0.001

Admission MBI p value †

281 65.1+25.4 (0-100)

48.3+29.8* (0-100) <0.001

70.0+22.0 (0-100)

46.9+26.4* (0-100) <0.001

69.2+22.8 (0-100)

50.5+26.4* (0-100) <0.001

64.7+25.8 (0-100)

46.7+29.7* (0-100) <0.001


153

Table 4-5: Clinical characteristics of 281 older adults aged >60 years according to nutritional status on admission (continued)

n SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278) Biochemical Well-nourished








(n=79) Serum albumin (mg/L) p value † >= 35mg/L < 35mg/L p value ‡

276 33.7+4.2 (18-43)

83(46) 98(54)

29.8+6.1* (15-41) <0.001 21(22)* 74(78) <0.001

33.5+4.5 (18-43)

82(46) 96(54)

30.6+5.7* (17-41) <0.001 13(25)* 40(75) 0.005

33.4+4.4 (17-43)

78(45) 97(55)

31.3+6.0* (18-41) 0.005 17(30) 39(70) 0.060

33.2+4.7 (15-43)

85(43) 111(57)

30.2+4.9* (15-41) <0.001 18(23)* 59(77) 0.002

TLC (x103/mm3) p value § >= 1.5x103/mm3 < 1.5x103/mm3

p value ‡

281 1.2 (0.3-4.0)

68(37) 116(63)

1.2 (0.1-3.4)

0.907 39(40) 58(60) 0.594

1.2 (0.1-4.0)

63(35) 119(65)

1.2 (0.3-3.4)

0.640 22(41) 32(59) 0.410

1.3 (0.3-4.0)

73(41) 106(59)

1.0* (0.1-2.6)

0.001 12(21)* 45(79) 0.007

1.3 (0.3-4.0)

81(41) 118(59)

1.0* (0.1-3.2)

0.009 25(32) 54(68) 0.161

Abbreviations: AMT: Abbreviated Mental Test; TLC: total lymphocyte count; MBI: Modified Barthel Index Results are expressed as n(%) or mean+SD(range). TLC was expressed as median (range) as it was a non-parametric variable. †‡§ Comparisons between well-nourished and malnourished participants within each malnutrition assessment method performed by independent samples t-test (†), Chi-squared test(‡) and Mann-Whitney Test (§) * Significantly different between the well-nourished and malnourished participants within the malnutrition assessment method, p<0.05 a Defined using DSM IV criteria354 by physician b Defined using a single question “Do you often feel sad or depressed?”355 c Abbreviated Mental Test356 – 10-item test with maximum of 10 points. Score <7 indicates cognitive impairment368 d Modified from original Severity of Illness Index358 into 4 level (of increasing severity from 1 to 4) burden of illness measure. e Charlson Comorbidity Index357 takes into account the number and the seriousness of comorbid diseases. Higher scores indicate a more severe

condition and prognosis. f Modified Barthel Index162 evaluates the level of assistance required for activities of daily living. Scoring is based on a continuous scale 0-100, with

100 indicating independent function. SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; CAMA: Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286


154

Table 4-6: Summary of patient characteristics significantly associated with malnutrition on admission as assessed by four

different malnutrition assessment methods

Characteristics SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278) Old age �

Institutionalised �

Poor appetite � � � �

Taste change �

Poor dentition �

Poor fitting dentures � � �

Swallowing impairment � � � �

Modified texture diet � � � �

Thickened fluid �

Dementia � � � �

Depression � � � �

Low AMT score � � � �

High Charlson Comorbidity Index � � �

Low serum albumin � � � �

Low TLC � � Poor pre-morbid and admission MBI

� � � �

Abbreviations: AMT: Abbreviated Mental Test; TLC: total lymphocyte count; MBI: Modified Barthel Index Characteristics in bold are associated with malnutrition regardless of the assessment methods of malnutrition


155

4.4 Discussion

4.4.1 Prevalence of malnutrition in hospitalised ol der adults

This study showed that the prevalence of malnutrition in hospitalised older adults (23-

35%) is within the range reported by other studies (12-79%) despite using different

malnutrition assessment methods (refer to Table 1-6). The variation in malnutrition

prevalence between the different assessment methods in the present study is smaller

compared to other studies71, 82, 83 where different assessment methods were also

applied within each sample (refer to Table 1-6 and 1-7).

When the same four malnutrition assessment methods used in this present study were

applied in other studies, similarly conducted with hospitalised older adults, variations

in prevalence were observed. Although SGA detected the highest prevalence of

malnutrition (35%) in the present study, it is lower than that reported in all the other

studies using a similar criterion (SGA: 41-79%)71, 93, 94, 113 (refer to Table 1-6 for more

details of the studies). This difference is possibly attributable to the differences in

case-mix and clinical conditions of the patients in the studies. In two studies where

critically or terminally ill were excluded, the prevalence was 41-48%93, 94. This result

is closer to the present study (35%). The study by Kyle et al (2002)71 in 392 medical

and surgical patients aged 60 years and above, reported a malnutrition prevalence of

79%. This is much higher than the results from this present study. They included

surgical patients, and terminally ill patients were not excluded. This difference in

patient profile may have contributed to the increased prevalence reported.

The malnutrition prevalence detected using MNA in the present study (23%) was

comparable to those reported by other studies (19-25%)20, 120, 121 (refer to Table 1-6


156

for more details of the studies). In this comparison, both the present study and those in

the literature had similar mean age and clinical diagnoses of the participants. In

addition, the MNA was developed and validated specifically in the elderly population.

4.4.2 Body Mass Index (BMI) cut-offs

Comparisons of malnutrition prevalence using anthropometric measures such as BMI

and CAMA were made more difficult with the different cut-offs and reference norms

used by different studies. Among the studies reviewed (refer to Table 1-6), the study

by Coelho et al118 was the only one conducted among hospitalised older adults that

used the same BMI cut-off (18.5kg/m2) as the present study. They found a 30%

malnutrition prevalence among 197 hospitalised elderly (>60 years) in Brazil. This is

similar to what was shown in the present study (26%).

As previously mentioned in Chapter 2 (section 2.2.2.1.1), the BMI classification

based on the assessment method of chronic energy deficiency (CED)260 adopted by

WHO (Table 2-2) supported the BMI cut-off of 18.5kg/m2. Although most studies

used BMI <20kg/m2 with malnutrition prevalence at 14-34%69, 71, 76 or BMI <23kg/m2

with a much higher prevalence at 52%74, these higher cut-offs were mainly applied in

studies of Caucasian populations and hence the validity of these cut-offs to define

malnutrition in the Asian population is unknown.

There are evidence to support the lower BMI cut-offs for overweight and obesity

among Asians and Singaporeans262, 264 due to the differences in body composition

between ethnic and racial groups. Although it may seem plausible that the BMI cut-

offs for underweight may also be lower based on the leftward shift of the distribution


157

curve, this may result in underestimation as only those who are severely malnourished

are identified. This explanation is based on the evidence that if higher body fat was

found in Asian adults with the same BMI as Caucasians264, the lower BMI range may

also indirectly reflect lower lean body mass among Asian adults than Caucasians.

Higher BMI cut-offs have been proposed for identifying underweight or malnutrition

risk in older adults267, 268. The lower malnutrition prevalence using the BMI

assessment (26%) compared to using the SGA (35%) in the present study might have

also suggested a higher cut-off. However, based on the smaller statures of the Asian

population, adopting the higher BMI cut-offs i.e. <20kg/m2 for underweight on the

other hand may potentially overestimate the prevalence of malnutrition (40%) in the

present study. No relevant cut-offs among Asian adults or elderly for

underweight/malnutrition have been suggested in the literature. It is possible that

using different cut-off values for assessing underweight and malnutrition among

Asians and Singaporeans may be necessary.

4.4.3 Estimation of BMI using knee height

Although it has been shown that knee height predicts standing height more closely

than demi-span or armspan359, there are limitations with the use of non-ethnic and

population specific predictive equations. These equations may overestimate stature300,

301 and hence underestimate the BMI. Therefore the selection of an appropriate knee

height equation has important implications on the accuracy of height estimation and

calculated BMI. Since there are no knee height equations available for Singaporeans,

the equation used in the present study was based on Hong Kong Chinese elderly

population253 as they most closely match the study sample population. It is however


158

uncertain if the knee height measured in this study over- or under-estimated the BMI

of the study population.

4.4.4 Application of CAMA

There are only three studies that have used CAMA to determine malnutrition among

hospitalised older adults79, 81, 82. Two studies used lower CAMA cut-offs compared to

the present study (men <21.4cm2, women <21.6cm2; 28%). In one of the two studies,

Potter et al (1995) studied 69 patients in the UK admitted from the community

without severe cognitive impairment. The authors used CAMA <16cm2 for men and

<16.9cm2 for women to identify patients with severe wasting malnutrition (prevalence

26%)81. A study by Reeves et al (1996) among 219 geriatric patients in an acute

assessment and rehabilitation unit in Switzerland used CAMA <5th percentile and

malnutrition prevalence of 36% was reported79. Both studies identified patients who

were more severely malnourished based on their lower cut-offs, yet the prevalence

was not lower compared to the present study. On the other hand, a third study by

Neumann et al (2005) on 133 older adults in rehabilitation in Australia used the same

CAMA cut-offs as the present study, and found that 20% were malnourished82. The

mean (+SD) BMI of their older adults was 26(+4.0) kg/m2 compared to 21.1(+4.0)

kg/m2 in the present study. The lower prevalence shown by Neumann et al82 could

possibly be due to the larger frame size and skeletal mass evident in Caucasian adults

compared to Chinese adults261.

Based on the literature reviewed, the study by Neumann et al82 was the only one to

have used CAMA <21.4cm2 for men and <21.6cm2 for women to define malnutrition

among hospitalised older adults. The lower cut-offs used in other studies only


159

identified patients who were more severely malnourished81, 300. Therefore higher

CAMA cut-offs were chosen in the present study to also identify those with moderate

muscle wasting (moderately malnourished)82.

4.4.5 Incomplete measurements for nutritional statu s

The MNA and BMI were completed only in 84% of participants in this study, mainly

due to the lack of admission weight measurement (n=43). The prevalence of

malnutrition (defined by SGA) was higher (51%) in those participants without

admission weight (incomplete MNA and BMI) than those with available admission

weight (32%). Lack of admission weight was also significantly associated (p<0.05)

with severity of illness and functional status on admission. These findings were not

unexpected as participants who were more severely ill and had poorer function on

admission would pose difficulties and limitations in their weight measurements.

Other studies which used the MNA and anthropometric measures for defining

malnutrition also reported similar rates of missing data for these parameters. It is

common to have inadequate data for assessment136. Studies using the MNA showed

only 66-87% hospitalised elderly patients completed the full MNA113, 120, 203. A recent

malnutrition study in a hospital in the UK by Stratton et al (2006) showed that weight

could be measured in only 56% of hospitalised elderly (n=150, mean age 85 years) on

admission202. Weight was obtained from either reliable recall or subjective criteria to

complete the nutritional screening using MUST. It was reported that those who could

not be weighed had a greater prevalence of malnutrition risk defined using the MUST

(70% vs 49%)202.


160

With the identified differences in nutritional status and clinical characteristics

associated with the study participants without admission weight measurement, the

exclusion of those with incomplete MNA and BMI assessments would likely

contribute to an underestimation of the prevalence of malnutrition. This was reflected

with the lower prevalence rates shown by the MNA (23%) and BMI (24%) in the

study. Both the MNA and BMI involve weight measurements and there are

difficulties associated with obtaining them in an acute care setting. Therefore if these

assessment methods are to be used and weight measurements are not available,

alternative approaches should be considered to assess the nutritional status of these

those who cannot be weighed. One option may be to modify the BMI-related question

in the MNA to accommodate the scoring of weight unavailability. Another

consideration would be to assign similar risk to patients who could not have their

admission weight taken as those who report weight loss.

4.4.6 Characteristics associated with malnutrition

The sociodemographic,appetite, chewing, swallowing, and clinical characteristics

associated with the malnourished participants in the present study were similar to

those that were commonly reported in other studies31, 33, 36, 44, 49 (summarised in Table

1-2). The present study confirms that the common risk factors associated with

malnutrition in the older adults as reported in other studies are also consistent with

the Singapore hospitalised older adults.

A few of these characteristics identified in the present study are particularly important

as their associations with malnutrition were evident regardless of the assessment

method used (summary Table 4-6). Poor appetite and swallowing impairment are


161

important factors influencing oral intake, choice of food and food quality, which have

been shown to influence nutritional status in hospitalised older adults31, 33. Clinical

conditions such as cognitive impairment, dementia and depression are commonly

known to be associated with malnutrition in the elderly31, 36, 37. The association

between poor functional status and poor nutritional status in the older adults is

frequently reported40, 43, 44, 164, although it is not clear whether poor functional status

precedes the decline in nutritional status or vice versa.

Although the other characteristics identified in the present study such as old age,

institutionalised care prior to admission, and poor dentition may not be consistently

evident across the different malnutrition assessment methods, it does not imply that

they are unimportant. Their associations with malnutrition have also been reported

(Table 1-2). The present study might not have identified additional or new patient

characteristics associated with poor nutritional status. Nevertheless, it has added new

data and knowledge to characterising the malnourished older adults on admission to a

Singapore acute care hospital. With a better profiling of these malnourished

hospitalised older adults on admission, closer attention and monitoring could be

provided to those who present with high risks i.e. the identified characteristics, even

before admission to hospital.


162

4.5 Conclusion

There was a high prevalence of malnutrition among hospitalised older adults in a

Singapore acute care hospital, with a quarter to a third of the older adults being

malnourished on admission. Results from this study were comparable to that reported

in other developed countries. As in other studies, the prevalence varied with the

malnutrition assessment methods used, with the SGA returning the highest

malnutrition prevalence at 35% and the MNA returning the lowest at 23%.

Among the four malnutrition assessment methods used in the present study, the

completion rates for the MNA and BMI were the lowest (84%) since both

assessments require admission weight that are often difficult to measure. About half

of those without admission weight were malnourished (when defined by the SGA),

highlighting that underestimation of malnutrition prevalence was likely to occur when

the MNA or BMI were used. In comparison, CAMA involves mainly arm

measurements which are relatively easier (completion rate 99%) to obtain than weight

and could be considered as an alternative assessment to the MNA and BMI. Although

the SGA includes questions on weight loss, it could still be completed even if weight

measurements were not available as no specific scoring was assigned to the weight

information as compared to the MNA questions. Therefore this study showed that the

SGA and CAMA were more likely to be used easily and accurately with a higher

completion rate among hospitalised older adults.

A range of patient characteristics were identified to be associated with malnutrition on

admission. Swallowing impairment, poor appetite, the need for modified textured

diets, dementia and depression occurred more frequently in the malnourished


163

participants. Significantly lower (p<0.05) pre-morbid and admission functional status,

cognitive function and serum albumin were also found among the malnourished

participants. It is important to assess and closely monitor patients presenting with

these characteristics prior to and upon hospital admission as they are possibly more

prone to developing malnutrition. Some of these clinical characteristics could be

identified and addressed at the community level for prevention and early detection of

malnutrition amongst those at higher risk. It would be less beneficial and effective if

these patients are only identified upon admission to the hospital.


164

5 Nutritional status and clinical outcomes


The consequences of malnutrition in hospitalised older adults are clinically important,

extensive and had been well-documented in many Western countries (discussed in

section 1.4). From the literature reviewed, it was shown that there is limited research

studying the predictive ability of malnutrition (independent of covariates such as

cognition, functional status, severity of disease, co-morbidities) on clinical outcomes.

Studies on the impact of malnutrition among hospitalised elderly patients in

Singapore are also very limited.

There is no “gold standard” for defining nutritional status due to the lack of a

universally accepted clinical assessment method for diagnosis of malnutrition. This

has resulted in a wide variation of malnutrition prevalence reported in other studies

(discussed in section 1.5), as well as in this study (refer to section 4.3). Due to the

impact of malnutrition on adverse clinical outcomes, the malnutrition assessment

methods can be validated against the clinical outcomes. Therefore an appropriate

malnutrition assessment method for the hospitalised older adults is one that can best

detect the associations between nutritional status and clinical outcomes.

This chapter therefore aims to describe the predictive association of nutritional status

(defined by SGA, MNA, BMI and CAMA) with five clinical outcomes (length of

stay, discharge to higher level care, readmission at 3-months, mortality and MBI at 6-

months) before and after adjustment for covariates. It will also compare the predictive

validities of nutritional status, defined by the four nutritional assessment tools and


165

indices, against the clinical outcomes. The malnutrition assessment method with the

best predictive ability would be identified and be used as the reference standard for

assessing nutritional status and for validation of the nutritional screening tools in

Chapter 6. This chapter will answer the third research question, “What are the

predictive abilities of the four malnutrition assessment methods, and which is most

suitable for use in hospitalised older adults?”

5.2 Methods

First, univariate analyses of the association between each of the clinical outcomes and

the nutritional status (using SGA, MNA, BMI, and CAMA) were performed. This was

followed by a multivariate analysis with adjustment for nine covariates (age, gender,

race, dementia, depression, number of prescribed drugs, severity of illness, Charlson

Comorbidity Index and admission MBI) previously described in Chapter 2 (section

2.2.3). Similar analyses of the clinical outcomes were also applied to participants with

and without admission weight. The outcome LOS was analysed both as a continuous

(median LOS) and categorical variable (LOS </>11days) in the univariate analysis.

The LOS was dichotomised into LOS >11days and LOS<11days, based on the

average LOS of the patients from the GRM unit.

Next, the validity of the each of the four malnutrition assessment methods was

evaluated against the clinical outcomes. ROC curve analysis of the probabilities from

each set of the multivariate regression models were performed against each clinical

outcome (except for 6-month MBI as it is a continuous outcome variable). The area

under the curve (AUC), sensitivity, specificity, PPV and NPV were compared to

determine the predictive validity of the four malnutrition assessment methods.


166

The multivariate associations for each of the malnutrition assessment method with

clinical outcomes were compared in addition to their predictive validity. The method

which showed the best predictive ability would be selected as the reference standard

for classifying nutritional status in the study population.

5.3 Results

5.3.1 Univariate relationships between nutritional status and

clinical outcomes

The univariate relationships between nutritional status and clinical outcomes are

presented in Table 5-1.

• Length of stay (LOS)

The median LOS was longer among the SGA- and BMI-defined malnourished than

well-nourished participants (11 vs 8 days, p<0.05). A significantly higher proportion

of SGA-defined malnourished participants had LOS > 11days (55% vs 38%, OR 1.94,

95% CI [1.18-3.20], p<0.05).

• Discharge to higher level care

Discharge to higher level care was not associated with malnutrition, regardless of

assessment method used.


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• Readmission at 3-months

Readmission rates at 3-months were significantly higher in the SGA- and MNA-

defined malnourished groups than in the well-nourished groups (48-49% vs 28-31%,

OR 2.15-2.42, 95% CI [1.12-4.15], p<0.05).

• Mortality at 6-months

All malnourished groups, except the BMI-defined group, had 2.5-3.5 times higher

mortality rates at 6-months (15-22% vs 6-8%, OR 2.97-4.30, 95% CI [1.11-9.35],

p<0.05) than the well-nourished groups. Although the association of BMI-defined

malnutrition with mortality at 6-months did not reach statistically significance

(p=0.13, p>0.05), the effect size (OR 2.18, 95% CI [0.80-5.93]) was clinically

significant.

• Modified Barthel Index at 6-months

Modified Barthel Index (MBI) at 6-months was significantly lower in the

malnourished than well-nourished groups, across all assessment methods of

malnutrition (49-76 vs 75-80, Β -[33.95-15.13], 95% CI [-42.37, -6.66], p<0.05).

Table 5-2 summarises the univariate relationships between nutritional status and

clinical outcomes. SGA-defined malnutrition, in comparison with other malnutrition

assessment methods, was significantly associated with the greatest number of clinical

outcomes (LOS >11 days, readmission at 3-months, mortality at 6-months, and MBI

at 6-months). This was followed by MNA-defined malnutrition which was similarly

associated with readmission at 3-months, and both mortality and MBI at 6-months.

However, MNA was not associated with LOS >11 days, unlike SGA. BMI-defined


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malnutrition achieved clinically significant association with three outcomes (LOS >11

days, mortality and MBI at 6-months), however only 6-month MBI was statistically

significant (p<0.05). CAMA-defined malnutrition also achieved clinically significant

association with three outcomes (3-month readmission, 6-month mortality and MBI),

with only mortality and MBI at 6-months being statistically significant (p<0.05).

Overall, as shown in Table 5-2, all the four malnutrition assessment methods were

associated with 6-month mortality (OR 2.18-4.30, 95% CI [0.80-9.35]) and 6-month

MBI (Β-[33.95-15.13], 95% CI [-42.37, -6.66], p<0.05). None of the malnutrition

assessment methods were significantly associated (p>0.05) with discharge to higher

level care.


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Table 5-1: Clinical outcomes of study participants (n=281) aged >60 years according to nutritional status as assessed by the four different malnutrition assessment methods

n SGA (n=281) MNA (n=236) BMI (n=236) CAMA (n=278) Outcomes Well-nourished








(n=79) LOS , days, mean+SD Median (Range) ΒΒΒΒ(95% CI)/p value† >/= 11 days^ <11 days OR(95% CI)/p value ‡

280 10.8+7.1 8(2-41)

2.9(1.0-4.8) 70(38) 113(62)

1.94(1.18-3.20)

13.7+8.6 11(3-50)*

0.03 53(55)* 44(45) 0.009

10.7+6.5 9(2-40)

1.9(-0.1-3.9) 74(41) 108(59)

1.17(0.63-2.16)

13.1+9.2 10(3-50)

0.068 24(44) 30(56) 0.62

10.2+6.0 8(2-40)

4.3(2.1-6.4) 68(38) 111(62)

1.81(0.99-3.31)

14.4+9.7 11(3-50)*

<0.001 30(53) 27(47) 0.051

11.5+7.7 9(2-42)

1.0(-1.0-3.1) 82(41) 117(59)

1.39(0.82-2.35)

12.5+7.9 10(3-50)

0.319 39(49) 40(51) 0.216

Discharge higher level care Yes No OR(95% CI)/p value ‡

277 55(30) 126(70)

0.85(0.49-1.48)

26(27) 70(73) 0.565

51(28) 130(72)

0.73(0.36-1.49)

12(22) 42(78) 0.386

46(26) 132(74)

1.22(0.63-2.36)

17(30) 40(70) 0.555

55(28) 144(72)

1.21(0.68-2.15)

24(32) 52(68) 0.518

3-month Readmission Yes No OR(95% CI)/p value ‡

261 50(28) 127(72)

2.42(1.41-4.15)

41(49)* 43(51) 0.001

51(29) 124(71)

2.15(1.12-4.12)

23(47)* 26(53) 0.021

57(33) 117(69)

1.06(0.54-2.06)

17(34) 33(66) 0.869

62(32) 129(68)

1.46(0.82-2.58)

28(41) 40(59) 0.196

6-month Mortality Yes No OR(95% CI)/p value ‡

279 11(6)

171(94) 4.30(1.97-9.35)

21(22)* 76(78) <0.001

10(6)

171(94) 2.97(1.11-7.96)

8(15)* 46(85) 0.033

11(6)

168(94) 2.18(0.80-5.93)

7(12) 49(88) 0.126

15(8)

183(92) 3.34(1.58-7.10)

17(22)* 62(78) 0.001

6-month MBI, mean+SD ΒΒΒΒ(95% CI)/p value†

246 75.2+29.7 -15.1(-23.6,-6.7)

60.0+34.3* 0.001

80.3+25.9 -33.6(-42.4, -25.5)

48.8+33.1* <0.001

77.5+28.1 -17.1(-26.6,-7.6)

60.4+34.1* <0.001

75.5+29.0 -19.1(-28.1,-10.2)

56.3+36.0* <0.001

Abbreviations: LOS: length of stay; MBI: Modified Barthel Index; Β(unstandardised coefficient); OR(CI): Odds Ratio (Confidence Interval) Results are expressed as n(%) ^LOS >/<11days based on the average LOS for the patients in GRM unit †‡ Comparisons between well-nourished and malnourished participants within each malnutrition assessment method performed by linear regression (†) and logistic regression (‡).(reference group: well-nourished participants) * Significantly different between the well-nourished and malnourished participants within each malnutrition assessment method, p<0.05 SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; CAMA: Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286


170

Table 5-2: Summary of univariate relationships between clinical outcomes and nutritional status as assessed by the four different malnutrition assessment methods (expressed in odds ratio, 95% confidence interval).

Length of Stay (LOS) >11 days^

Discharge Higher Level Care

3-month Readmission

6-month Mortality

6-month MBI #

n=280 n=277 n=261 n=279 n=246 SGA –Malnourished

1.94 (1.18-3.20)

p=0.009

NS 2.42 (1.41-4.15)

p=0.001

4.30 (1.97-9.35)

p<0.001

-15.13 (-23.59, -6.66)

p=0.001

*n=236 n=235 n=224 n=235 n=216 MNA – Malnourished

NS NS 2.15 (1.12-4.12)

p=0.021

2.97 (1.11-7.96)

p=0.033

-33.95 (-42.37,-25.53)

p<0.001

BMI – Malnourished

NS

NS NS NS -17.09 (-26.56,-7.62)

p<0.001 n=278 n=275 n=259 n=277 n=244 CAMA – Malnourished

NS NS NS

3.34 (1.58-7.10)

p=0.001

-19.14 (-28.10,-10.19)

p<0.001 Abbreviations: SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment; BMI: body mass index; CAMA: corrected arm muscle area; NS= Not statistically and clinically significant Results from logistic regression expressed as odds ratio (95% confidence interval) with relevant p values in italics. ^LOS >/<11days based on the average LOS for the patients in GRM unit

# Results from linear regression expressed as Β (95% confidence interval) with relevant p values in italics. *The n values for MNA and BMI are the same based on the number of participants for whom weight/height was available.

Bold = statistically significant, p<0.05.

Refer to Table 5-1 for more details.


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5.3.2 Multivariate relationships between nutritiona l status and

clinical outcomes

When the associations between nutritional status and clinical outcomes were further

adjusted for the nine covariates (age, race, dementia, depression, severity of illness,

Charlson Comorbidity Index, number of prescribed drugs and admission MBI) in

multivariate logistic and linear regressions (refer to Appendix Table A-9 to Table A-

28 for detailed analyses), the associations between nutritional status and clinical

outcomes were mainly attenuated or became insignificant. Table 5-3 summarises the

multivariate relationships between nutritional status and clinical outcomes.

SGA-determined malnutrition remained statistically significant and predictive of LOS

>11days (OR 2.45, 95% CI [1.27-4.71], p<0.05 in Table 5-3) after adjustment for the

whole range of covariates. The relationship between nutritional status (defined by all

the assessment methods) and 6-month mortality failed to remain statistically

significant in the multivariate model.

Most of the univariate associations between 6-month MBI and nutritional status

(SGA, BMI, CAMA) became insignificant after adjustment for covariates. Only

MNA-defined malnutrition remained statistically significant (Β -14.02, 95% CI [-

21.37, -6.67], p<0.05 in Table 5-3) in its association with 6-month MBI even though

its effect was largely attenuated with adjustment (Β -33.95, 95% CI [-42.37,-25.53],

p<0.05, without adjustment in Table 5-2).


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Table 5-3: Summary of multivariate∞ relationships between clinical outcomes and nutritional status as assessed by the four different malnutrition assessment methods (expressed in odds ratio, 95% confidence interval)


Discharge Higher

Level Care

3-month Readmission

6-month Mortality

6-month MBI #

Model sample, n 224 223 222 224 206 2.45

(1.27-4.71) p=0.007

NS NS SGA –Malnourished +additional analysis using full sample for available for SGA

n=267 2.12*

(1.17-3.83) p=0.013

NS

n=260 1.90*

(1.05-3.42) p=0.034

n=266 3.04*

(1.28-7.18) p=0.011

NS

MNA – Malnourished

NS NS NS NS -14.02 (-21.37,-6.67)

p<0.001

BMI – Malnourished

NS NS NS NS NS

NS CAMA – Malnourished +additional analysis using full sample for available for CAMA

NS NS NS

n=264 2.48*

(1.05-5.84) p=0.038

NS

Abbreviations: SGA: subjective global assessment; MNA: mini-nutritional assessment; BMI: body mass index; CAMA: corrected arm muscle area; NS= Not statistically and clinically significant ∞∞∞∞All models are adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI. Results from multivariate logistic regression expressed as odds ratio (95% confidence interval) with relevant p values in italics.; Bold = Statistically significant, p<0.05; Refer to Appendix Tables A-9 to A-28 for details. ^ LOS >/<11days based on the average LOS for the patients in GRM unit # Results from multiple linear regression expressed as Β (95% confidence interval) with relevant p values in italic. + The model samples in the multivariate analyses are based on participants with weight/height data as a standard sample is applied for comparative regression analysis of the outcomes. * Significant associations between nutritional status and clinical outcomes from additional multivariate analysis performed using the full sample.


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5.3.3 Comparison of participants with and without a dmission

weight and clinical outcomes

In the previous chapter (Table 4-1), comparisons of the sociodemographic, clinical

characteristics, and nutritional status were made between the participants with and without

admission weight. In this section, the clinical outcomes will be compared and presented in

Table 5-4.

Participants without admission weight recorded presented with poorer clinical outcomes

compared to those with admission weight recorded. At the univariate analyses (refer to Table

5-4), participants without admission weight had significantly longer LOS (Β 3.67, 95% CI

[1.18-6.16], p<0.05), higher 6-month mortality (OR 5.82, 95% CI [2.62-12.94], p<0.05), and

6-month lower MBI (Β-25.45, 95% CI [-37.46,-13.43], p<0.05).

After adjustment for the covariates, lack of admission weight remained predictive of 6-month

mortality (OR 4.99, 95% CI [1.86-13.40], p<0.05 in Table 5-4). However, the association

between the lack of admission weight and LOS, and 6-month MBI became statistically

insignificant in the multiple linear regression analyses.


174

Table 5-4: Comparison of clinical outcomes between participants with and without admission weight measurements

Outcomes N (n)#

Admission weight available

(n=238)

Admission weight not available (n=43)

Odds Ratio

95% Confidence

Interval

p value‡ Adjusted∞∞∞∞ Odds Ratio

Adjusted 95%

Confidence Interval

Adjusted p value‡

LOS , days, mean+SD Median (range) >/= 11 days^ <11 days

281 (267)

11.2+7.3 9 (2-50) 99(42) 138(58)

14.9+9.4 12(2-42) 24(56) 19(44)

3.67+ 1.76

1.18-6.16 0.92-3.39

0.004† 0.09

NR 1.27

NR

0.55-2.93

0.886† 0.578


277 (264)

64(27) 172(73)

17(42) 24(58)

1.90

0.96-3.77

0.065

0.69

0.30-1.60

0.385

3-month Readmission Yes No

272 (259)

82(35) 152(65)

17(45) 21(55)

1.50

0.75-3.00

0.251

1.06

0.45-2.49

0.898

6-month Mortality Yes No

278 (265)

18(8)

217(92)

14(33) 29(67)

5.82*

2.62-12.94

<0.001

4.99*

1.86-13.40

0.001

6-month MBI, mean+SD

246 (234)

73.5+30.3

48.0+34.7

-25.45+ -37.46, -13.43

<0.001†

NR

NR

0.976†

Abbreviations: LOS: length of stay; MBI: Modified Barthel Index; NR: Not relevant Results are expressed as n(%) # N=sample size in univariate analysis; n=sample size in multivariate analysis ∞∞∞∞Adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI ^ LOS >/<11days based on the average LOS for the patients in GRM unit †‡ Comparisons between participants with and without admission weight, performed by linear regression (†) and logistic regression (‡) (reference group: participants with admission weight) * Participants without admission weight significantly associated with 6-month mortality with and without adjustment of covariates, p<0.05 + Β(unstandardised coefficient) from linear regression, participants without admission weight had significantly longer LOS and lower 6-month MBI score


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5.3.4 Predictive power of nutritional assessment to ols and indices

on clinical outcomes

The results from the multivariate regression models in section 5.3.2 only reflected the

predictive associations of nutritional status with clinical outcomes. The regression

models are unable to determine the predictive validity of the nutritional assessment

tools and indices in identifying clinical outcomes. Therefore the ROC curve analysis

of the multivariate regression model for each malnutrition assessment method was

performed against the clinical outcomes.

Table 5-5 shows the summary of the AUC for each of the malnutrition assessment

methods against the clinical outcomes (refer to Appendix: Tables A-33 to A-36 for

full details of the ROC curve analysis). The SGA model showed the highest AUC for

LOS >11 days (0.75) in comparison to the other malnutrition models. Both MNA and

SGA models showed the highest AUC for 3-month readmission at (0.67) and 6-month

mortality (0.73). All four malnutrition models showed the same AUC for discharge to

higher level care (0.72). Overall, SGA showed the highest average AUC (0.72) across

all clinical outcomes.


176

Table 5-5: Summary of the AUC from ROC curve analysis for each malnutrition assessment method against the clinical outcomes


n=224

Discharge Higher

Level Care n=223

3-month Readmission

n=222

6-month Mortality

n=224

Average AUC

SGA –Model

0.75

0.72

0.67

0.73

0.72

MNA – Model

0.72

0.72

0.67

0.73

0.71

BMI – Model

0.73

0.72

0.66

0.72

0.71

CAMA –Model

0.72

0.72

0.66

0.68

0.70

Abbreviations: AUC: area under the curve; ROC: Receiver Operating Characteristic; SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment; BMI: body mass index; CAMA: corrected arm muscle area

Results are expressed as AUC for each nutrition models against the respective clinical outcomes (excluding 6-month MBI as it is a continuous outcome variable and could not be applied in ROC curve analysis)

^ LOS >/<11days based on the average LOS for the patients in GRM unit

Bold = Highest AUC among the nutrition models for each outcome;

∞∞∞∞All multivariate malnutrition models include age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI as covariates. Details of the ROC curve analysis of each malnutrition model against the clinical outcomes are shown in Appendix Tables A-33 to A-36.


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5.4 Discussion

5.4.1 Nutritional status and clinical outcomes

Overall, the clinical outcomes (LOS >11 days, 3-month readmission, 6-month

mortality and 6-month MBI) of the malnourished participants were poorer

compared to the well-nourished participants across the four assessment methods of

malnutrition in this study (refer to Table 5-1). Prevalence of increased LOS (>11

days) was 45% higher, 3-month readmission rates were up to 75% higher, 6-month

mortality rates were up to 3.5 times higher, and 6-month functional status (MBI

score) was up to 40% lower, among the malnourished participants. These

associations of malnutrition with adverse clinical outcomes shown here were

comparable with those reported in other studies. The latter studies showed up to

three times higher in-hospital to 1 year mortality rates87, 94, 99, 110, 120, 123, 157, 158, up

to 50% longer LOS82, 83, 111, 116, 119, 120, 136, 139, up to three times poorer functional

status82, 94, 157, and two times higher readmission rates85 in malnourished compared

to the well-nourished patients.

The present study did not show the association of malnutrition with discharge to

higher level of care. This is in contrast to results shown by Covinsky et al (1999)94

(n=369, age>70 years, US) and Neumann et al (2005)82 (n=133, age>65years,

Australia) where malnourished older adults (defined by SGA rating C and MNA

score<24, respectively) in hospital and rehabilitation settings respectively, were

more likely to be discharged to higher level care (OR 3.22, 95% CI [1.05-9.87]

and OR 2.29, 95% CI [1.09-4.80], respectively). It is possible that the differences

in functional status of the patients on admission may have an influence on the


178

impact of malnutrition on patients’ discharge destination. However, it was shown

that even with94 or without82 adjustment for admission functional status in both

studies, the association between malnutrition and discharge destination remained.

Although discharge destination could be an indirect measure for functional

outcome, it could also be a service-based outcome. It is possible that differences in

care settings and healthcare systems play a role in explaining this difference in

results. The healthcare system in Singapore encourages the older adults to be cared

for in the community than in long-term care institutions. Therefore hospitalised

older adults, who require a higher level of care after discharge, need not

necessarily be discharged to another institutionalised care facilities i.e. nursing

home.

Many of the significant associations between nutritional status and clinical

outcomes evident at the univariate level failed to remain significant after

adjustment for the comprehensive range of covariates in the present study. Only

the association between SGA-defined malnutrition and LOS >11 days (OR 2.45,

95% CI [1.27-4.71], p<0.05), and MNA-defined malnutrition and 6-month MBI

(Β -14.02, 95% CI [-21.37, -6.67], p<0.05) remained statistically significant in the

multivariate analysis. In fact, most of the studies which have reported significant

associations between malnutrition and poor clinical outcomes were analysed only

at the univariate level, and hence they may have overestimated the predictive

associations between nutrition status and clinical outcomes (refer section 1.6,

Table 1-10).


179

There were only a few studies which had adjusted for covariates when establishing

the relationship between nutritional status and clinical outcomes in the hospitalised

older adults61, 62, 74, 79, 87, 94, 151, 157, 158 (refer to section 1.6, Table 1-11). The

number of covariates included in most of these studies ranged from 2 to 6, and

only one study94 included similar number of covariates (nine covariates) as the

present study. A few of these studies also included functional status on admission

as one of the covariates in their multivariate analysis79, 94, 151, 157, 158, just like the

present study. After adjustment for the various covariates in these studies,

malnutrition (assessed using different assessment methods i.e. SGA, BMI, serum

albumin, CAMA) remained significantly associated with mortality62, 74, 79, 94, 157, 158,

hospital readmission151, and functional status94. These results contrasted with what

the present study has shown where the malnutrition associations with 6-month

mortality and 3-month readmission were shown to be insignificant after

adjustment and only LOS>11days remained significant (OR 2.45, 95% CI [1.27-

4.71], p<0.05).

Some plausible explanations for the associations of malnutrition with clinical

outcomes to remain despite the adjustment of covariates in the other studies could

be the use of fewer62, 74 and different covariates151, 157, 158 in these study analyses

compared to the present study. Most of the previous studies only included some,

but not all the important covariates like comorbidities, functional status and

cognitive impairment. Therefore, it is possible that the associations between

malnutrition and clinical outcomes reported in these studies were still an

overestimation. In addition, the different assessment methods of malnutrition e.g.


180

serum albumin used in other studies62, 151, 157 and the different follow-up period62,

74, 79, 94, 157 for outcomes may also limit like-for-like data comparison.

Covinsky et al’s (1999)94 study was the only one conducted in hospitalised older

adults, using a similar number and type of covariates (comorbidity, disease

severity, cognitive and functional status) and malnutrition assessment method

(SGA) as the present study. In their study of 369 hospitalised older adults aged

>70 years in the US (median LOS not reported), malnutrition on admission,

particularly severe malnutrition, was predictive of (i) 90-day and 12-month

mortality (OR 3.26, 95% CI [1.52-6.96], and OR 2.83, 95% CI [1.47-5.45]), (ii)

dependence in at least 1 ADL at 3-month (OR 2.81, 95% CI [1.06-7.46]), and (iii)

discharge to nursing home at 1 year (OR 3.22, 95% CI [1.05-9.87]). The main

difference between the present study and that conducted by Covinsky at al94 was

that the latter study showed significant associations between nutritional status and

outcomes only among the severely malnourished patients. In the present study the

moderately and severely malnourished (SGA B and C) participants were combined

for analysis as the severely malnourished group (n=17) was relatively small to be

analysed separately.


181

5.4.2 Different sample size and characteristics in univariate and

multivariate analyses

For comparison of the relationships across the various malnutrition assessment

methods and clinical outcomes in this study, a uniform sample (n=206-224,

depending on the outcome variable) which only included participants with

complete data for all the variables in the regression model, was selected in the

multivariate analysis. As there were more missing data for BMI and MNA

assessments, a considerable number of study participants included in the SGA and

CAMA univariate analyses (n=244-280) were excluded from the comparative

multivariate analyses (n=206-224).

The smaller sample used in the multivariate analysis for SGA- and CAMA-

defined malnutrition and clinical outcomes could possibly reduce the power of the

study and increase the risk of type two error (false negative results). Therefore it is

likely that the strong associations of nutritional status with clinical outcomes

shown at the univariate level in the present study have been attenuated to an

insignificant level at the multivariate analysis with the smaller sample. This might

have contributed to the present study reporting that SGA-defined malnutrition

does not have a predictive ability on clinical outcome which was otherwise shown

by Covinsky et al (1999)94. Likewise, the small sample size might have diminished

the potential impact of CAMA-defined malnutrition on 6-month mortality and

other clinical outcomes previously shown in both hospital and community older

adults79, 229, 277, 287.


182

Besides the possible risk for type two error with the smaller sample, the

participants’ characteristics of the smaller sample used in the multivariate analyses

were slightly different from the full sample used in the univariate analyses for the

SGA and CAMA. Participants in the full sample had significantly poorer

functional status on admission (mean MBI 59.3+28.1 vs 64.7+25.0, p<0.05), and

were slightly more malnourished (35% vs 32%, p>0.05). Other characteristics

such as age, gender, race, depression, dementia, severity of illness, and

comorbidities were similar in both samples (p>0.05). With the full sample having

poorer functional status on admission and relatively more malnourished, it is likely

that the effect size for the association between nutritional status and clinical

outcomes may have been greater if this sample was applied in the multivariate

analyses. It is possible that any association of nutritional status with clinical

outcomes may be unveiled with the full sample.

To confirm if the above explanations are valid, multivariate analysis with the full

sample (from the univariate analysis) for the SGA against all the clinical outcomes

was performed. When these analyses with the full sample were done (refer to

Table 5-3 and Appendix Tables A-29 to A-32), the SGA was shown to be

significantly associated with LOS >11days (n=267, OR 2.12, 95% CI [1.17-3.83],

p<0.05), readmission at 3-month (n=260, OR 1.90, 95% CI [1.05-3.42], p<0.05)

and mortality at 6-month (n=266, OR 3.04, 95% CI [1.28-7.18], p<0.05).

Compared to the original analysis based on the smaller sample, 3-month

readmission (n=222, OR 1.49, 95% CI [0.78-2.84], p=0.23) and 6-month mortality

(n=224, OR 2.36, 95% CI [0.80-7.01], p=0.12) became significantly associated

with malnutrition (by SGA) and showed a greater effect size. The predictive


183

association of the SGA with mortality shown in this new analysis is now

consistent with Covinsky et al94.

Likewise, when the same sample from the univariate analysis for CAMA was

included in the multivariate analysis (refer to Table 5-3 and Appendix Tables A-31

to A-32), CAMA was shown to be significantly associated with 6-month mortality

(n=264, OR 2.48, 95% CI [1.05-5.84], p<0.05) compared to the original analysis

(n=224, OR 1.82, 95% CI [0.60-5.51], P=0.29). Most of the previous studies

which showed the significant association between low CAMA levels and

increased mortality were conducted mainly among the elderly in the community229,

277, 287. There was only one study by Muhlethaler et al 373 (n=219, age>65years)

which demonstrated the predictive ability of CAMA on long-term mortality at 4.5

years (OR 1.8, 95% CI [1.3-2.6]) among hospitalised geriatric patients in

Switzerland. The association of CAMA-defined malnutrition with short-term

mortality at 6-months was similarly demonstrated in the present study (OR 2.48,

95% CI [1.05-5.84], p<0.05) with this new analysis.

These additional (post-hoc) analyses revealed important results from the present

study. The results strengthened the ability of the SGA as a significant predictor of

clinical outcomes, beyond its only association with LOS (in the original analyses).

These findings demonstrated the independent and significant associations between

nutritional status and clinical outcomes regardless of the patients’ clinical and

medical conditions.


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5.4.3 Nutritional status and functional outcome

Nutritional status was strongly associated with functional status at 6-month across

all assessment methods of malnutrition in the present study (unadjusted Β -[33.95-

15.13], 95% CI [-42.37, -6.66], p<0.05). The MNA-defined malnutrition was the

only one which remained significant after adjustment for covariates (adjusted Β -

14.02, 95% CI [-21.37, -6.67], p<0.05). The association between poor functional

and nutritional status with the elderly has been shown in several studies and in

different settings such as the community146, 147, rehabilitation82 and hospitals94, 157.

Two of the studies were cross sectional studies, one of which was conducted in the

community in the US147 (n=3053, >65 years) and one in a Swedish hospital157

(n=337, mean age 81 years). BMI (<15th percentile, <20 or <22 kg/m2) was used

as the main nutritional status indicator in both of these studies. Both showed a

significant association between lower BMI and a poorer functional status in the

univariate analysis. This association was maintained in the multivariate analysis in

the US study by Galanos et al (1994)147, but not in the Swedish study by Flodin et

al (2000)157. A strong association (univariate) between poor nutritional and poor

functional status on admission was shown in the present study (refer to Table 4-5)

as was reported in the other cross sectional studies147, 157.

The other three studies were longitudinal studies. In the first study, Bannerman et

al (2002)146 showed that in a sample of 1272 healthy community-dwelling

Australian older adults (mean age 77.5+5.5 years), a lower BMI (<20 kg/m2,

which indicated underweight) reduced the risk of self-reported limited physical

function at 2-years follow-up with adjustment for ten covariates (OR 0.31, 95% CI

[0.15-0.67]). This protective effect of a lower BMI on functional status at 2-years


185

shown by Bannerman et al contrasted with the results in the present study that a

lower BMI (<18.5 kg/m2) was associated with poorer functional status at 6-months

post discharge (unadjusted Β-17.09, 95% CI [-25.56,-7.62], p<0.05). This

difference may be explained firstly by the fact that different BMI cut-offs were

used in the studies. Secondly, the assessment methods of functional limitations

used in both studies were different. The physical function assessment in the

Bannerman et al study included fewer components than that in the MBI used in the

present study. Thirdly, the participants in the comparative study consisted of

younger and healthier community-dwelling older adults. All these factors could

have influenced the association between nutritional and functional status.

A second Australian study with 133 elderly patients (>65 years) of a rehabilitation

hospital found poor nutritional status as defined by the MNA (total score <24),

BMI (<22 kg/m2) and CAMA (<21.4 cm2 for male; <21.6 cm2 for female) was

significantly associated with poorer functional status (defined by MBI through

phone interview) at 90 days, after adjusting for admission MBI82. These results

were consistent with the present study where the MNA-defined malnutrition (total

score <17) was significantly associated with lower 6-month MBI after adjustment

for covariates (Β -14.02, 95% CI [-21.37,-6.67], p<0.05). However the main

difference between the two studies was the number of covariates used in the

analysis, with Neumann et al82 using only one covariate (admission MBI) as

compared to the present where nine covariates were applied including admission

MBI. This suggests that the association between nutritional and functional status

shown in the present study is less likely to be influenced by non nutrition-related

factors.


186

The third study, by Covinsky et al (1999)94 in hospitalised elderly >70 years in the

US (n=369) showed in the multivariate analysis that severely malnourished

patients on admission (defined by SGA rating C) were more likely to be dependent

in at least one ADL 90 days after discharge (OR 2.81, 95% CI [1.06-7.46]). Unlike

Covinsky et al’s94, the strong univariate association between poor functional

outcome at 6 -months and poor nutritional status (defined by SGA rating B+C) at

baseline failed to hold in the present study after the baseline functional status and

other covariates were adjusted for. The MNA-defined malnutrition was the only

nutritional assessment tool that remained significantly associated with 6-month

MBI after adjustment for covariates (Β -14.02, 95% CI [-21.37,-6.67], p<0.05).

The different results shown in the present study and Covinsky et al94 could be

attributed to the use of different measurement tools for functional (admission and

follow-up) and nutritional status. In addition, Covinsky et al94 separated the

moderately and severely malnourished patients in their study for analysis, whereas

both moderately and severely malnourished participants were analysed as a group

in the present study. This could further explain the difference in the relationship

between nutritional status and functional outcome between the studies, as the

significant association was only shown among those who were severely

malnourished (even after adjustment for covariates) in the study by Covinsky et

al94.

The relationship between nutritional status and functional outcomes is complex

and reciprocal94. A strong association between poor nutritional status and poor

admission functional status, as well as between poor nutrition status and functional


187

outcomes after discharge, have been shown here and in the other cross sectional

studies147, 157. However, it appears in the present study that the baseline functional

status has a stronger influence on the functional outcome than baseline nutritional

status since the association with the latter failed to hold (for most of the

malnutrition assessment methods) or was attenuated after admission functional

status was adjusted for. Therefore, the size and significance of association between

nutritional status and functional outcomes demonstrated in the present study may

have been smaller compared to other studies82, 146, 147 as admission functional

status (together with many other covariates) was adjusted for in the multivariate

analysis. From the results here, only the MNA-defined malnutrition remained

statistically significant in its impact on functional status at 6-months (adjusted Β-

14.02, 95% CI [-21.37, -6.67], p<0.05). This relationship shown between the

MNA and functional status at 6-month is possibly influenced by the questions

relating to the assessment of functional status in the MNA tool.

5.4.4 BMI and clinical outcomes

Compared to the other malnutrition assessment methods evaluated in this study,

BMI showed the poorest results. At the univariate analysis (refer to Table 5-2),

BMI-defined malnutrition was significantly associated with only 6-month MBI (Β

-17.09, 95% CI [-26.56, -7.62], p<0.05). BMI was the only malnutrition

assessment method that did not demonstrate any statistical significant relationship

with all the clinical outcomes in the multivariate analysis (refer to Table 5-3).


188

The poor predictive performance of the BMI shown in the present study contrasted

with that shown by other studies74, 82, 146, 266. Due to the lack of weight and height

measurement in 16% of the participants, a smaller sample of the BMI data was

available for the regression analysis. This could have affected the power of the

sample size and potentially caused a type two error. Similarly for the MNA

analysis, a type two error could also have occurred due to the reduced sample size

from missing BMI data. Clinically, the application of the MNA or BMI may

potentially underestimate the impact of malnutrition on clinical outcomes if no

alternative measurements are used to assess those whose admission weight could

not be taken.

5.4.5 Lack of admission weight measurement and clin ical

outcomes

Participants without weight taken on admission were shown to be at higher risk of

6-month mortality (adjusted OR 4.99, 95% CI [1.86-13.40], p<0.05 in Table 5-4).

This risk was greater compared to that associated with malnourished participants

(defined by SGA) on admission (adjusted OR 3.04, 95% CI [1.28-7.18], p<0.05 in

Table 5-3). This greater impact on adverse clinical outcomes shown in those

without admission weight measurements even after adjustment for covariates

highlighted the important implication of weight measurement on admission.


189

It is common for studies to exclude participants with missing nutrition data in the

analysis113, 120, 203, therefore the characteristics of these participants are usually

unknown. From the literature reviewed, the relationship between weight

availability and clinical outcomes had only been reported in one study. This study

by Stratton et al (2006)202 conducted in the UK with 150 hospitalised elderly

(mean age 85 years) showed that amongst those without admission weight (44%),

in-hospital mortality was higher (40% vs 12%) and median LOS (50, range 13-87

days vs 17, range 14-20 days) was longer than those who could be weighed.

Weight was unavailable in a higher proportion of participants (44%) in this study

than in the present study (15%).

Participants who did not have their admission weight measured in the present

study were sicker and had poorer functional capacity on admission (refer to Table

4-1). These reasons may have prevented them from being transferred safely to

chair scales. As shown in this study, patients who were unfit for weighing were

more likely to experience poor clinical outcomes (refer to Table 5-4) and had a

higher prevalence of malnutrition (refer to Table 4-1). Hence lack of weight

measurements should be treated as a risk and prompt a follow-up action. It is

crucial that these patients can be assessed using alternative measurements or tools

which are independent of the weight. It is also important to consider a nutritional

assessment method that can be applied to all patients in routine clinical practice, so

that no one will be excluded from the appropriate intervention due to incomplete

data.


190

The other implication of the results was that participants excluded from the

MNA/BMI and outcome analysis (due to the lack of admission weight) were at

this increased risk of 6-month mortality (adjusted OR 4.99, 95% CI [1.86-13.40],

p<0.05 in Table 5-4). The absence of a statistically significant relationship

between MNA/BMI and 6-month mortality could be attributed in part by this

missing data.

5.4.6 Predictive power of nutritional assessment to ols and

indices on clinical outcomes

The ROC curve analysis is commonly used to determine and compare the

predictive power of medical diagnostic tools374. This approach has also been

commonly adopted in the validation of nutrition screening tools. However its

application in evaluating the predictive validity of the different malnutrition

assessment methods may not have been as commonly reported.

Based on the comparisons of the area under the ROC curve (AUC) of the various

malnutrition models against the clinical outcomes, both the SGA and MNA

models had comparable performance (predictive validity) in predicting the

outcomes (discharge to higher level care, 3-month readmission, and mortality, see

Table 5.4). However, the SGA was slightly superior at predicting service

outcomes e.g. LOS >11 days (AUC 0.75), than MNA (AUC 0.72), and it also

showed the highest average AUC across all the clinical outcomes. The main

limitation for the MNA was the relatively lower completion rate (84%) compared

to SGA (100%). This was reported earlier in section 3.3.


191

Both the nutritional assessment tools (SGA and MNA) demonstrated better

predictive ability for more clinical outcomes as compared to the single objective

measures (BMI and CAMA). The SGA and MNA tools include more dimensions

e.g. socio-psychological, medical and functional factors, in the assessment of

malnutrition instead of focusing on a single factor. Therefore they are more likely

to be predictive of poor clinical outcomes317, 319, 320. Between SGA and MNA, the

present study showed that the SGA was a significant predictor of more clinical

outcomes than the MNA. The SGA was also more likely to be collected accurately

than MNA in the present study as weight measurement was not as essential in the

final rating of the SGA as it was for the MNA, resulting in a higher completion

rate for the former.


192

5.5 Conclusion

Malnutrition on admission was associated with increased LOS (>11days), 3-month

readmission, 6-month mortality, and 6-month functional status in Singapore

hospitalised older adults. Malnutrition, defined mainly by the SGA, remained a

significant predictor of these important clinical outcomes (LOS>11days, 3-month

readmission and 6-month mortality) independent of the influences from a

comprehensive range of covariates. The risk of 6-month mortality was three times

higher among the malnourished older patients, regardless of their medical, clinical

and functional conditions on admission. Although the associations between poor

nutritional status and adverse clinical outcomes have been frequently reported in

hospitalised older adults, only a few studies have shown the influence of

malnutrition on clinical outcomes prospectively like what the present study did. In

addition, the independent effect of malnutrition on adverse outcomes was only

shown in a few studies with adjustment for only a limited number of covariates

(refer to Table 1-11). Therefore results from this study highlighted the independent

impact of nutritional status on clinical outcomes in hospitalised older adults.

Comparing the predictive validity of the four malnutrition assessment methods on

clinical outcomes, both the SGA and MNA (in logistic regression models) were

comparable in their diagnostic performances with similar AUCs for all the

outcomes. However, the average AUC for the SGA across the outcomes was the

highest (0.72) and it also was able to predict the most clinical outcomes at both the

univariate (4 out of 5) and multivariate analyses (3 out of 5). An additional

practical consideration was the higher completion rate for the SGA (100%)

compared to the MNA (84%), which would allow wider application of the SGA to


193

the population. Participants excluded from the MNA analysis due to the lack of

admission weight was shown to be at increased risk of adverse clinical outcome

(6-month mortality: adjusted OR 4.99, 95% CI [1.86-13.40], p<0.05). This

potentially underestimated the impact of malnutrition on clinical outcomes when

the MNA was used. Therefore when all these results were considered in

combination, they supported the selection of the SGA as the most appropriate

reference standard for defining malnutrition for the population and setting in the

present study.

In the next chapter 6, the validation of nutritional screening tools against the

selected reference standard for malnutrition assessment –SGA, is reported.


194

6 Criterion-related validity of nutrition screening tools


Chapters 4 and 5 highlighted the high prevalence (23-35%) of malnutrition

amongst hospitalised older adults in a Singapore acute hospital and its impact on

adverse clinical outcomes. It is important that nutrition screening is implemented

with a validated screening tool. Although there are several screening tools

available (refer section 1.6.2), none have been validated in Singapore hospitalised

older adults.

The aim of this Chapter is to validate four nutrition screening tools, TTSH NST,

NRS 2002, MNA-SF, and SNAQ© (refer to section 1.7.2 for details of tools)

against the malnutrition assessment method (SGA) identified in Chapter 5, and

hence to determine the most suitable screening tool for use in this population. The

screening tools were selected based on the following criteria: (i) developed and

routinely applied at the study site (TTSH NST); (ii) recommended by professional

body (ESPEN) for application in hospitalised patients (NRS 2002); (iii) widely

used with the elderly (MNA-SF); and (iv) simple questions (SNAQ©) (refer to

Chapter 2 section 2.2.1 for details of justification). At the same time, the level of

nutritional risk among the participants as defined by the different nutrition

screening tools will also be described. This Chapter will answer the fourth

research question, “Which of the four nutrition screening tools has the best validity

in predicting malnutrition when compared against the SGA (the recommended


195

malnutrition assessment method identified in the previous chapter), and hence

most suitable for use in hospitalised older adults?”

6.2 Methods

All four nutrition screening tools were completed by the dietetic technician on all

participants upon hospital admission. The interview for each participant was

completed within one session, in no particular order (Appendix: Data forms).

Many participants were unsure or had unquantifiable weight loss in response to the

question “Did you lose weight unintentionally?” and the original SNAQ© tool did

not have the option for scoring such responses. Therefore a modified SNAQ©

screening tool was created in this study to allow for scoring these responses. To

limit the bias in scoring, the final scores for each tool were not computed until the

data were entered into the statistical software for analysis. The TTSH NST

completed by the ward nurses as part of the hospital standard care was also

recorded to evaluate its inter-rater reliability.

The completed screening tools were validated against the SGA using the original

cut-offs for the tools. The diagnostic performance was assessed based on

sensitivity, specificity, positive predictive value (PPV) and negative predictive

value (NPV). ROC curve analysis was performed for all the screening tools

against the SGA, and their AUCs were compared. Once the screening tool with the

best diagnostic performance was identified, further validation of the selected

screening tool was performed against the clinical outcomes with adjustment of the

covariates (age, gender, race, dementia, depression, severity of illness, Charlson


196

Comorbidity Index, number of prescribed drugs and admission MBI) using

multiple regression analysis to confirm its selection.

6.3 Results

6.3.1 Classification of malnutrition risk

Participants’ malnutrition risks as assessed by the various nutrition screening tools

are shown in Table 6-1 (6-81%). Nutritional screening was completed for all the

participants using all the screening tools, except for the MNA-SF which was only

completed for 84% of participants. The lower completion rate for the MNA-SF

was mainly attributable to the unavailability of the BMI due to the lack of weight

and/or height measures on admission.

Despite having the lowest completion rate, the MNA-SF detected the highest

malnutrition risk (81%). The MNA-SF had the most number of questions and the

highest possible total score of 14. The lowest malnutrition risk was detected with

SNAQ© (6%) with the lowest possible total score of 5. The TTSH NST, SNAQ-

Modified and NRS 2002 showed similar levels of malnutrition risk among the

participants (35-42%). However, this study observed a large discrepancy between

the malnutrition risk identified by the dietetic technician (42%) and the ward

nurses (17%) when both used the TTSH NST. The level of agreement between the

assessors was 68% and the inter-rater reliability between them was poor as shown

with a kappa estimate of 0.28372.


197

Table 6-1: Classification of malnutrition risk in older adults >60 years by the various nutrition screening tools completed by dietetic technician

TTSH NST (4 questions, possible total score 10) Frequency Proportion % Risk level (n=281) No risk, score <4 At risk, score >4

162 119

58 42

Risk level assessed by nurses (n=278) No risk, score <4 At risk, score >4

231 47

83 17

NRS 2002166 (3 questions, possible total score 7) Risk level (n=281) No risk, score <3 At risk, score >3

176 105

63 37

MNA-SF185 (6 questions, possible total score 14)+

Risk level (n=236)# No risk, score >11 At risk, score <11

44 192

19 81

SNAQ©179 (3 questions, possible total score 5) Risk level (n=281) No risk, score <2 At risk, score >2

265 16

94 6

SNAQ –Modified^ (3 questions, possible total score 5)

Risk level (n=281) No risk, score <2 At risk, score >2

184 97

65 35

Abbreviations: TTSH NST: Tan Tock Seng Hospital Nutrition Screening Tool (Appendix Table A-4); NRS 2002166: Nutrition Risk Screening 2002; MNA-SF185: Mini Nutritional Assessment Short Form; SNAQ©179: Short Nutritional Assessment Questionnaire; AUC: Area under the curve + All the tools, except MNA-SF, reflect risk of malnutrition with higher scores. # The sample for MNA-SF was lower than other tools due to the unavailability of BMI from 45 participants. ^ Unsure or non-quantifiable weight loss was scored ‘1’ in the SNAQ-Modified tool, instead of ‘0’ in the original SNAQ© tool


198

6.3.2 Diagnostic validity of nutrition screening to ols

Based on the results and conclusion from Chapter 5, SGA was selected as the

reference standard for validation of the nutrition screening tools. Table 6-2 shows

the diagnostic performances of all the nutrition screening tools when compared

against SGA-determined nutritional status. Figure 6-1 shows the ROC curves of

the all nutrition screening tools against the SGA from the ROC analyses. As a

higher score on the MNA-SF indicates a lower malnutrition risk in contrast to all

the other screening tools (higher score indicates a higher risk), the ROC curve

analysis for the MNA-SF was performed separately and is shown in Figure 6-2.

The TTSH NST performed by the dietetic technician showed the best diagnostic

performance amongst all the nutrition screening tools, with the highest AUC of

0.865 and sensitivity, specificity, positive predictive value (PPV) and negative

predictive value (NPV) at 84%, 79%, 68% and 90%, respectively. The TTSH

NST routinely completed by the nurses had an AUC of 0.695 and sensitivity,

specificity, PPV and NPV at 36%, 93%, 72% and 74%, respectively.

The NRS 2002 and SNAQ-Modified were comparable in their diagnostic

performances with the TTSH NST performed by the dietetic technician, except

that their sensitivities were lower (NRS 2002: 69%; SNAQ-Modified: 70%). The

SNAQ© showed the lowest sensitivity (17%) and NPV (69%) and the MNA-SF

showed the lowest specificity (27%) and PPV (39%).


199

For the TTSH NST, it was shown that the optimal cut-off for all participants

(AUC=0.87, Figure 6-3), and those aged >85 years old (AUC=0.85, Figure 6-4)

remained at score four.


200

Table 6-2: Validation of nutrition screening tools against SGA determined nutritional status

TTSH NST-

dietetic technician (n=281)

TTSH NST- nurses (n=278)

NRS 2002166 (n=281)

MNA-SF185

(n=236) #

SNAQ©

179 (n=281)

SNAQ-Modified

^ (n=281)

Sensitivity, % 84 36 69 100 17 70

Specificity, % 79 93 79 27 100 84

Positive Predictive Value (PPV), %

68

72 64 39 100 70

Negative Predictive Value (NPV), %

90 74 83 100 69 84

AUC (n=235) 0.865* 0.695 0.783 0.839 0.761 0.836

Abbreviations: TTSH NST: Tan Tock Seng Hospital Nutrition Screening Tool (Appendix

Table A-4); NRS2002166: Nutrition Risk Screening 2002; MNA-SF185: Mini Nutritional

Assessment Short Form; SNAQ©179: Short Nutritional Assessment Questionnaire; AUC: Area

under the curve

Sensitivity, specificity, PPV and NPV were derived based on the original cut-offs of the

screening tools shown in Table 1. # The sample for MNA-SF was lower than other tools due to the unavailability of BMI from 45

participants.

^ Unsure or non-quantifiable weight loss was scored ‘1’ in the SNAQ-Modified tool, instead of

‘0’ in the original SNAQ© tool

*Highest AUC among all screening tools compared against a standardised sample n=235.


201

Figure 6-1: ROC curves of nutrition screening tools against SGA (n=235)

Abbreviations: TTSH NST: Tan Tock Seng Hospital Nutrition Screening Tool (Appendix Table A-4); NRS 2002166: Nutrition Risk Screening 2002; MNA-SF185: Mini Nutritional Assessment Short Form; SNAQ©179: Short Nutritional Assessment Questionnaire. Since the samples for the different tools varied, a standardized sample was used in this analysis to compare the various screening tools. All AUC values remained unchanged with a standard sample and they are shown in Table 2. Adjusted SNAQ total score was derived from the SNAQ-modified tool. Unsure or non-quantifiable weight loss was scored ‘1’ in the SNAQ-Modified tool, instead of ‘0’ in the original SNAQ tool MNA-SF was shown separately in Figure 2 as higher MNA-SF score indicated “no risk” for malnutrition instead of “at risk”, which was opposite from the other tools. NST completed by dietetic technician showed the highest AUC at 0.865 compared to the other tools.


202

Figure 6-2: ROC curve of MNA-SF against SGA (n=235)

Abbreviations: MNA-SF185: Mini Nutritional Assessment Short Form MNA-SF was shown separately in this figure as higher MNA-SF score indicated “no risk” for malnutrition instead of “at risk”, which was opposite from the other tools.


203

Figure 6-3: ROC curve of TTSH NST (completed by the dietetic technician) against SGA for all participants aged >60 years (n=281)

AUC= 0.87


204

Figure 6-4: ROC curve of TTSH NST (completed by the dietetic technician) against SGA for participants aged >85 years (n=95)

AUC=0.85


205

6.3.3 Predictive validity (prognostic performance) of TTSH NST

In addition to evaluating the diagnostic performance of the nutrition screening

tools, some studies166, 200, 203, 215, 224 analysed their predictive validity as patients

who are at risk of malnutrition were more likely to have adverse outcomes e.g.

LOS215. From the results presented earlier in this Chapter, it was evident that

TTSH NST had the best diagnostic performance against SGA compared to the

other three nutrition screening tools. The predictive validity of the TTSH NST

was hence evaluated to further assess its clinical value as a nutrition screening

tool. If confirmed so, this would further strengthen its selection.

The clinical outcomes of participants assessed by TTSH NST-defined nutritional

risk (assessed by the dietetic technician) are presented in Table 6-3. Participants at

risk of malnutrition had significantly longer LOS (13.3 vs 10.6 days, p<0.05),

lower MBI score at 6-months (59.7 vs 77.6, p<0.05), higher risk of staying longer

(LOS>11days) in hospital (OR 1.83, 95% CI [1.13-2.97], p<0.05), and higher risk

of mortality at 6-months (OR 2.53, 95% CI [1.19-5.42], p<0.05).

After adjustment for covariates (age, race, dementia, depression, severity of

illness, Charlson Comorbidity Index, number of prescribed drugs and admission

MBI), the TTSH NST)-defined nutritional risk (assessed by the dietetic technician

was shown to be predictive of LOS >11days (OR 1.89, 95% CI [1.05-3.35],

p<0.05). This is presented in Table 6-3. Mortality and MBI at 6-months became

insignificant after adjustment for the comprehensive range of covariates.


206

Table 6-3: TTSH NST-defined malnutrition risk as assessed by the dietetic technician and clinical outcomes

Outcomes N (n)#

TTSH NST- At risk

malnutrition (n=118)

TTSH NST- Not at risk

malnutrition (n=162)

Odds Ratio

95% Confidence

Interval

p value‡ Adjusted∞∞∞∞ Odds Ratio

Adjusted 95%

Confidence Interval

Adjusted p value‡


280 (267)

13.3+8.3 11 (3-50) 62(53) 56(47)

10.6+7.1 8(2-41) 61(38) 101(62)

2.7+

1.83*

0.9-4.5

1.13-2.97

0.004† 0.013

2.2+

1.89*

0.5-4.0

1.05-3.35

0.014† 0.033


277 (264)

38(33) 79(67)

43(27) 117(73)

1.31

0.78-2.21

0.311

1.17

0.64-2.14

0.619

3-month readmission Yes No

273 (260)

47(41) 68(59)

52(33) 106(67)

1.41

0.86-2.32

0.177

0.56

0.57-1.82

0.957

6-month mortality Yes No

279 (266)

20(17) 98(83)

12(8)

149(92)

2.53*

1.19-5.42

0.014

0.57

0.71-4.01

0.239


246 (234)

59.7+34.8

77.6+27.7

-17.9+ -25.8, -10.0

<0.001†

NR

NR

0.163†

Abbreviations: TTSH NST: Tan Tock Seng Hospital Nutrition Screening Tool; LOS: length of stay; MBI: Modified Barthel Index; NR: Not relevant Results are expressed as n(%) # N=sample size in univariate analysis; n=sample size in multivariate analysis ^ LOS >/<11days based on the average LOS for the patients in GRM unit ∞∞∞∞ Adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI †‡ Comparisons between participants at risk and not at risk of malnutrition, performed by linear regression (†) and logistic regression (‡) (reference group: participants not at risk of malnutrition) * Participants at risk of malnutrition significantly associated with outcome, p<0.05 + Β (unstandardised coefficient) from linear regression, participants at risk of malnutrition had significantly longer LOS and lower 6-month MBI score.


207

6.4 Discussion

6.4.1 Evaluation of TTSH NST

The TTSH NST showed the best diagnostic performance when compared against the

SGA in this study. It had 100% completion rate, with the highest AUC of 0.865, and

the best balance between sensitivity (84%), specificity (79%), positive predictive

value (PPV; 68%) and negative predictive value (NPV; 90%). This means that the

TTSH NST was able to accurately identify 84% of the malnourished participants as

‘at risk’, and 79% of the well-nourished participants as ‘no risk’. Among those

screened to be ‘at risk’, 68% were malnourished, and 90% of those screened as ‘no

risk’ were well-nourished. The TTSH NST was able to effectively exclude those who

were well-nourished. Even though 32% of those screened as ‘at risk’ were well-

nourished, the TTSH NST serves the function of a nutrition screening tool which

includes identifying those at risk of malnutrition.

The optimal cut-off for the TTSH NST remained at score four even among the oldest

old hospitalised patients (aged >85 years), showing that the tool is applicable across

the age spectrum of the older adults. In addition, the TTSH NST also demonstrated

good prognostic performance for LOS >11days even after adjustment for a

comprehensive range of covariates. These evaluation results further strengthen the

validity and usefulness of the TTSH NST.

However the main limitation for the TTSH NST is the discrepancies in screening

results when it was administered by different assessors. The diagnostic performance

of the TTSH NST was poorer when completed by the nurses than by the dietetic


208

technician (refer to Table 6-2). The inter-rater reliability of TTSH NST between the

dietitians and nurses was >90% when the tool was developed and first implemented

(Appendix: TTSH Medical Board Paper on TTSH NST). However, the current study

showed a lower inter-rater reliability (kappa estimate 0.28) between the dietetic

technician and nurses who completed the screening routinely.

The malnutrition risk assessments performed by the nurses in this study were part of

the routine assessments for all newly admitted patients according to the hospital

nutrition screening policy. Although nurses in the hospital had received training from

the dietitians on the administration of the TTSH NST, it is possible that they were still

not adequately trained, or did not recognise the importance of screening accurately. In

comparison, the assessment by the dietetic technician was performed by a single

assessor who had more content knowledge and nutrition training than the nurses. As

the dietetic technician was acutely aware that the study was to assess the malnutrition

risk of the participants and to evaluate the four screening tools, she would be more

conscientious in applying the tool to obtain more accurate data.

6.4.2 Poor diagnostic performance of MNA-SF

One of the main shortcomings of the MNA-SF identified in the evaluation was the

reliance on BMI calculation. The completion rate for the MNA-SF (84%) was lower

than for the other screening tools (100%) where the calculation of BMI was not

required. Higher completion rates were usually observed in the use of nutrition

screening tools which do not require measurements of weight or BMI179, 203.


209

The MNA-SF did not perform well in the present study, when compared to other

studies213, 375. Although the present study showed a very high sensitivity (100%,

Table 6-2) for the MNA-SF using the original cut-off score of 11, its specificity

(27%) was very poor. This means that it was able to identify all the participants who

were malnourished, but at the same time 73% (1-specificity) of the well-nourished

participants were incorrectly identified with malnutrition risk (high false positive).

Borowiak and Kostka375 (2003) validated the MNA-SF against the full MNA among

311 community and institutionalised elderly aged >65 years in Poland. Its sensitivity

in the community-living elderly (74%) may not be as high as that in the present

study, but the specificity was much higher (95%). Similarly when Rubenstein et al213

(2001) validated the MNA-SF against the full MNA among 881 elderly (mean age 76

years) predominantly from the community (74%) in France, Spain and New Mexico,

both the sensitivity (98%) and specificity (100%) were shown to be high.

A plausible explanation for the poor performance of the MNA-SF in the present

study could be the differences in care settings. In the previous studies213, 375, the

participants were predominantly community-dwelling older adults and were younger

than the older hospitalised group in the present study. During hospitalisation, the

acute clinical conditions of the patients were usually associated with poorer

functional status. On admission to the hospital, the MNA may potentially identify

many of the older adults to be at risk of malnutrition due to several functional-related

questions in the MNA which are not specific to poor nutrition e.g. poor mobility, not

living independently and inability to feed without assistance. All these may explain

the lower specificity of the MNA-SF.


210

Another reason may be that the MNA-SF is culturally sensitive223. For example, the

BMI cut-offs used for scoring in the MNA-SF may not be appropriate for Asians and

the diet-related questions may not be applicable to another culture where the dietary

habits and food choices differ. It is possible that the MNA-SF may not perform

equally well in an Asian population and may be less suitable for application to

Singapore older adults.

The other possible reason may relate to the different validation references used (i.e.

SGA vs full MNA). One would expect the diagnostic performance of a screening tool

to be better when validated against a reference which consists of similar questions.

For example, one would expect good performance when the MNA-SF is validated

against the full MNA, which the former was derived from. However, it was not

shown to be so all the time. In a separate validation study conducted by Cohendy et

al376 (2001), the MNA-SF was validated against the full MNA (malnourished, score

<17) among 408 preoperative ambulatory older adults in France. The MNA-SF

showed very good sensitivity and NPV (both 100%), but poorer specificity (70%)

and PPV (19%). Cohendy et al376 validated the MNA-SF against patients with the

full MNA score <17 for classification of malnutrition. However, the other studies

validated the MNA-SF against patients classified as either “at risk of undernutrition”

or “malnourished” (MNA score <23.5) in the full MNA 213, 375. The key lies in the

difference between the reference groups for validation i.e. malnourished vs at risk

and malnourished, between studies.

Overall performance of the MNA-SF in the present study was inferior to other

studies. However, the AUC for the MNA-SF (0.839) shown in the present study was


211

close to that of the TTSH NST. Therefore the MNA-SF may still be useful if a

different cut-off is established, and some question modifications are considered.

6.4.3 Nutrition Risk Screening 2002 (NRS 2002)

Few studies have validated the NRS 2002 in hospitalised older adults and with mixed

results203, 206, 215, 216. Drescher et al206 showed that the higher NRS 2002 score

indicating increased malnutrition risk was associated with reduced serum prealbumin

and retinol-binding protein (biochemical markers of protein malnutrition) in 104

geriatric hospitalised patients from Switzerland (median age 84 years, range 78-89

years). Raslan et al216 showed in their sample of 705 Brazilian hospitalised patients

(169 patients age >65 years) that the NRS 2002 was predictive of severe

complications (OR2.6, 95% CI [1.1-6.4]) and death (OR 3.9, 95% CI [1.2-13.1]).

Similarly, the NRS 2002 was shown by Martins et al215 to be an independent risk

factor for longer LOS (OR 2.25, 95% CI [1.03-4.88]) among 207 hospitalised elderly

patients (mean age 74+7 years) in Portugal215. However the German study by Bauer

et al203 that compared the NRS 2002 against serum albumin and LOS in 121 geriatric

patients (mean age 80+8 years) did not reveal any significant associations. In

addition, validity (i.e. sensitivity and specificity) was not reported in all these study

and none had compared it against the SGA203, 206, 215, 216.

From the literature reviewed, only one other study224 had validated the NRS 2002

against the SGA. Kyle et al224 compared the diagnostic performance of the NRS 2002

against the SGA among 995 hospitalised patients in Switzerland (mean age 51+22

years). They reported a higher specificity (93%) and PPV (85%) and lower sensitivity

(62%) and NPV (79%) for NRS 2002224 than that reported in the present study (Table


212

6-2). The differences between the studies could be related to the different age and

types of patients.

Overall, the NRS 2002 has been shown to be predictive of adverse clinical outcomes

in hospitalised older adults. In the present study, the diagnostic performance of the

NRS 2002 is close to that shown among younger hospital patients. With improved

sensitivity, the NRS 2002 can be a potentially useful screening tool for use with

hospitalised older adults.

6.4.4 SNAQ-Modified Tool

The SNAQ-Modified tool detected a higher malnutrition risk level (35%) compared to

the original SNAQ© tool (6%). The difference between the two SNAQ tools lies in the

scoring of the question on weight loss. In the original SNAQ© tool, responses such as

“unsure” or “yes, unsure” were classified under “no weight loss” and given a score of

“0”. During the study implementation, we found that up to 63% of participants were

unsure if they had experienced weight loss or were unable to quantity the loss. This

led to the decision of modify the SNAQ©, so these responses were scored “1” instead.

This additional scoring of the weight response made a significant difference to the

proportion of participants classified as “at risk”. This clearly showed that participants

with uncertain or non-quantifiable weight loss should be considered differently from

those with no distinct weight loss reported, and they should be assigned a certain level

of nutrition risk.

The SNAQ© tool was developed for the general hospitalised adults and has never

been validated in the hospitalised elderly population, it 170, 179. When the original


213

SNAQ© tool was validated among 297 patients (mean age 61+7 years, 49% internal

medicine patients) against malnutrition (determined by BMI and weight loss), its

AUC (0.85), specificity (83%) and PPV (70%) were comparable to the performance

of the SNAQ-Modified tool used in the present study (Table 6-2). However, the

sensitivity (79% vs 70%) and NPV (89% vs 84%) were slightly better in the previous

validation study179 where the original SNAQ© tool was used.

These slight differences may be attributable to the different mean age and patient mix

between the validation population and the present study sample; the latter consisted

of medical geriatric patients. Moreover, the different nutrition reference standards

(BMI/weight loss vs SGA) used for validation of the screening tools in both studies

could also influence the tool performances. Therefore the SNAQ-Modified tool

which requires less than five minutes to administer, needs no calculations179 and does

not require a BMI measure, has the potential to be applied to hospitalised older

adults.


214

6.5 Conclusion

From the comparisons of the diagnostic performances of the four nutrition screening

tools, the TTSH NST (when completed by the dietetic technician) is clearly the most

appropriate nutrition screening tool to be applied to hospitalised older adults in

Singapore. The TTSH NST had 100% completion rate and the best diagnostic

performance amongst all the nutrition screening tools when compared against the

SGA, with the highest AUC (0.865) and the best balance between sensitivity (84%),

specificity (79%), positive predictive value (PPV; 68%) and negative predictive value

(NPV; 90%). However for the TTSH NST to achieve the good diagnostic

performance shown, it may have to be completed by a dietetic technician. Besides its

good diagnostic performance for nutritional status defined by the SGA, the TTSH

NST also demonstrated good prognostic performance for LOS >11days even after

adjustment for a comprehensive range of covariates. The TTSH NST is a valid and

useful nutrition screening tool, at the same optimal cut-off score of 4, even among the

oldest old hospitalised patients (aged >85 years) in Singapore if administered by a

dietetic technician.

Limitations and difficulties surrounding the measurements of weight and height were

reported in this study. Many older adults in Singapore do not monitor or remember

their weight therefore the poor knowledge of weight history among the study

participants is not surprising. In addition, healthcare institutions and facilities in

Singapore do not always measure and record patients’ weight. This limited the

suitability and application of the MNA-SF in the study population. Although the NRS

2002 and SNAQ (modified version) also demonstrated potential application in the

study group, they were not developed from the local population. Therefore, to


215

consider the use of these tools, further modification and validation of these tools

would be required prior to their adoption in the local population.

With the high prevalence of malnutrition among hospitalised older adults reported in

this study, there must be earlier and better recognition and treatment of malnourished

patients. All patients aged >60 years admitted to the hospital should be screened using

the TTSH NST administered by a dietetic technician to determine their malnutrition

risk. If identified to be at risk, they should undergo a comprehensive nutritional

assessment by a dietitian, including use of the SGA to determine their nutritional

status. This would allow appropriate, timely nutritional intervention to be

implemented.

Currently, the study hospital is using the TTSH NST administered by the nurses to

screen all patients on admission and the SGA to assess nutritional status of those

identified to be at risk of malnutrition. Therefore the study provides an evidence base

for the use of the current screening (TTSH NST) and assessment (SGA) tools at

TTSH. However, the study results emerged that performance of the TTSH NST is

enhanced when administered by a dietetic technician. The best personnel to conduct

the routine nutritional screening, which is currently performed by the nurses, may

have to be reviewed. One option is to recruit more dietetic technicians to undertake all

the screening. Another alternative is to provide and facilitate better training for the

nurses to more accurately administer the TTSH NST. The nurses will need to

recognise that nutrition screening is integral to the overall patient assessment and care

so that they will take ownership of the process.


216

7 Changes in anthropometric measurements and

nutritional status during hospitalisation, and impa ct

on clinical outcomes


Despite the high prevalence of malnutrition among hospitalised patients, poor

recognition and under-treatment of malnutrition remain a concern (previously

discussed in section 1.5.5). As a result, further nutritional depletion can occur during

hospitalisation and lead to worsened nutritional status among malnourished patients.

Both well and malnourished patients are potentially at risk of decline in nutritional

status during admission. Risk factors for this deterioration are previously discussed in

section 1.4 and include inadequate nutritional care, severity of illness, medication side

effects, and anorexia. Unfortunately, the monitoring and documentation of nutritional

status during hospitalisation of older adults has not been as widely reported as the

reporting of prevalence of malnutrition.

Nutritional status has been closely associated with clinical outcomes amongst

hospitalised older adults (section 1.4). The present study data are in line with results

from other studies, showing that malnutrition upon hospital admission independently

predicts adverse clinical outcomes (previously discussed in Chapter 5). Although

older adults are vulnerable to further decline in nutritional status during

hospitalisation (see section 1.5.5), few studies have investigated the extent and impact

of such a decline in status during the course of an admission on clinical outcomes.


217

The aims of this Chapter are to examine the changes in anthropometric measurements

and nutritional status of older adults during hospital admission and the prospective

association with clinical, functional and system outcomes. It will answer the fifth

research question: “What are the changes in anthropometric measures and nutritional

status of older adults during hospital admission and the impact of reduced nutritional

status on clinical outcomes?”. In this Chapter, a review would also be done to discuss

the level of malnutrition recognition and dietitian referral of the study participants

based on routine care in the hospital.

7.2 Methods

Weight, MAC, TSF, MAMC, MAMA and CAMA measurements and SGA were

repeated by the dietitian (YP Lim) not more than two days prior to discharge. These

discharge anthropometric measures were compared with those measured on admission

using paired sample t-tests and Wilcoxon Signed Ranks test, with mean change (+SD)

in measurements presented. Changes in the classification of nutritional status by SGA

on admission and discharge were compared using McNemar test. The MNA was not

repeated as a few of its questions are related to characteristics associated with acute

illness and they tend to improve over an admission when patient’s clinical condition

improves over time20. For example, the scoring for the questions on mobility and

living independently (refer to Appendix Table A-2, Question C and G) may improve

as the patient recovers from the acute illness. Therefore the difference in the MNA

score on admission and at discharge may not be indicative of the change in nutritional

status.


218

For participants with a reduction in any of the anthropometric parameters (on a pair-

wise basis) during admission, the median net and percentage change from baseline

(admission) for each of these anthropometric measurements were calculated.

Percentage weight loss during admission (relative to baseline admission weight) was

computed individually in participants with available admission and discharge weights.

Weight loss >1% per week of admission was used to define decline in nutritional

status236, 366 (refer to section 2.2.4.2 for more details) as no similar cut-offs for decline

were available for the other arm anthropometric measurements. Percentage weight

loss per week was calculated using the equation: (weight loss during

hospitalisation/admission weight) x (7 days/LOS) x 100%.

Chi-square tests were applied to compare the appetite, chewing, swallowing, clinical

characteristics and nutritional status on admission, between participants with weight

loss >1%, and weight loss <1% per week of hospitalisation. Factors associated with

decline in nutritional status (weight loss >1%) during admission were identified.

To determine the impact of decline in nutritional status (weight loss >1% vs weight

loss <1%) during hospitalisation on clinical outcomes (LOS>11days, discharge to

higher level care, 3-month readmission, 6-month mortality and MBI), univariate and

multivariate analyses were performed.

The recognition of malnutrition upon hospital admission and referral to the dietitian

during admission based on standard routine care were reviewed from the participants’

medical records at discharge. Referrals to the dietitian were made by the nurses based

on the nutrition screening score (performed by the nurses routinely) or by the doctors


219

based on their clinical judgement. The level of malnutrition recognition (by nurses)

was evaluated by comparing the proportion of those identified as at risk upon

admission through the routine nutrition screening, against the total malnourished

participants (assessed by SGA in the study). The frequency of referral to the dietitan

was expressed as a proportion against the frequency of those who were malnourished

(defined by SGA), as well as those with decline in nutritional status (weight loss

>1%) during admission.

The timeliness of referral to the dietitian was based on the median time to referral

after hospital admission, comparing between the well-nourished and malnourished

participants, and comparing between those with and without decline in nutritional

status, using Mann-Whitney test.


220

7.3 Results

7.3.1 Change in anthropometric measurements and nut ritional

status during hospitalisation

The median length of hospitalisation was 9 days (range: 2-50 days). Table 7-1 shows

the anthropometric measurements and nutritional status of older adults on admission

to and at discharge from an acute hospital in Singapore. Weight, MAC, TSF, MAMC

and MAMA significantly (p<0.05) decreased during hospital admission as shown

using paired-sample t-test and Wilcoxon Signed Ranks test with no significant

(p>0.05) change in CAMA and the SGA nutritional status classification at discharge.

Table 7-2 shows the frequency and proportions of participants with decline in

anthropometric measures and nutritional status during hospitalisation, the median

decline and median percentage decline from baseline (admission) measurements. The

proportion of participants with reduced anthropometric measures and nutritional

status ranged from 4% based on SGA to 44% based on weight loss. The median

percentage decline in these anthropometric measures ranged from 2.1% to 9.1%, with

TSF showing the greatest decline.


221

Table 7-1: Anthropometric measurements and nutritional status of older adults (>60 years) on admission to and upon discharge from an acute care hospital [median LOS 9 days (range 2-50 days)]

n# Admission Discharge Mean change (Discharge-Admission)

p value

Weight (kg)+ 193 50.4+10.4 (27.0-79.9)

50.2+10.2 (28.9-79.0)*

-0.3+1.7 (-6.8-9.2)

0.033†

MAC (cm) 246 24.7+4.0 (14.0-37.5)

24.5+4.0 (14.2-37.5)*

-0.2+0.6 (-2.6-2.7)

<0.001†

TSF (mm) 246 12.1+6.1 (3.0-37.0)

11.8+6.0 (3.0-27.0)*

-0.3+1.2 (-6.0-4.5)

<0.001§

MAMC (cm) 246 20.8+2.7 (12.6-27.3)

20.8+2.7 (12.6-27.4)*

-0.1+0.6 (-2.4-2.7)

0.033†

MAMA (cm2) 246 34.7+8.9 (12.4-58.4)

34.5+8.8 (12.5-58.8)*

-0.3+1.9 (-7.9-7.6)

0.020†

CAMA (cm2)a

246 26.8+8.7

(5.9-51.1) 26.8+8.7 (6.5-51.6)

0.01+1.93 (-7.9-8.1)

0.924†

SGAb Well-nourished Malnourished

277 181(65) 96(35)

176(64) 101(36)

-

0.227‡

Abbreviations: MAC: mid-arm circumference; TSF: triceps skinfold thickness; MAMC: mid-

arm muscle circumference; MAMA: mid-arm muscle area; CAMA: corrected arm muscle area;

SGA: Subjective Global Assessment. #Only participants with both admission and discharge measurements were included.

Results are expressed as mean+SD(range) or n(%). + Both admission and discharge weight measurements were only available for 69% participants

as many were unfit to be weighed or weighing was missed out prior discharge.

†‡§Comparisons between admission and discharge measurements were performed by paired

sample t-test(†), McNemar test (‡) and Wilcoxon Signed Ranks test (§).

*Significantly lower than admission measurements, p<0.05. a CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female),

Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female) 286; b SGA: Well-nourished =SGA rating A (well-nourished),

Malnourished=SGA rating B (moderately malnourished) and C (severely

malnourished) 235


222

Table 7-2: Frequency and magnitude of decline in anthropometric measurements and nutritional status of older adults (>60 years) with admission and discharge assessment [median LOS 9 days (range 2-50 days)]

n# Frequency of

decline

[Frequency (%)]

Median decline

[Median (range)]

Per cent decline

from admission

[Median% (range)]

Weight (kg)+ 193 85(44) 1.0 (0.1-6.8) ^ 2.1 (0.2-16.6)

Weight loss >1% per week +*

193 52(27) 1.8 (1.0-10.0) 3.6 (0.9-16.6)

MAC (cm) 246 106(43) 0.5 (0.1-2.6) 2.1 (0.4-11.0)

TSF (mm) 246 82(33) 1.0 (0.5-6.0) 9.1 (2.6-35.3)

MAMC (cm) 246 99(40) 0.5 (0.0-2.4) 2.3 (0.1-11.2)

MAMA (cm2) 246 99(40) 1.5 (0.1-7.9) 4.5 (0.1-21.1)

CAMA (cm2)a 246 92(37) 1.3 (0.0-7.9) 4.9 (0.2-31.1)

SGA 277 11(4) NA NA

Abbreviations: MAC: mid-arm circumference; TSF: triceps skinfold thickness; MAMC: mid-

arm muscle circumference; MAMA: mid-arm muscle area; CAMA: corrected arm muscle area;

SGA: Subjective Global Assessment. #Only participants with both admission and discharge measurements were included.

^ Median length of stay for participants with weight loss during hospitalisation = 11 days (4-40

days) + Both admission and discharge weight measurements were only available for 69% participants

as many were unfit to be weighed or weighing was missed out prior to discharge.

* Per cent weight loss per week = (weight loss during hospitalisation/admission weight) x (7

days/LOS) x 100%; computed only in participants with both admission and discharge weights

taken. Only participants with both admission and discharge weight measurements were

included.


223

7.3.1.1 Weight loss

Only 193 participants (69%) had their weight taken both on admission and at

discharge as many were unfit to be weighed or weighing was missed prior to

discharge. Of these 193 participants, 28% were malnourished (defined by SGA) on

admission, and 44% experienced weight loss (median 1.0kg, range 0.1-6.8kg) during

hospitalisation (Table 7-2). Forty-three per cent of those defined by SGA as

malnourished and 45% of those well-nourished on admission lost weight during

admission (p=1.00).

Based on those who lost weight during admission (n=85), those defined by SGA on

admission as malnourished (27%) lost a median of 0.9kg (range 0.2-6.8kg) and those

identified as well-nourished (73%) lost 1.2kg (range 0.1-6.4kg). The difference in the

magnitude of weight loss between the two groups were not statistically significant

(p=0.84).

The median LOS for the participants who experienced weight loss (n=85) was 11

days (range 4-40 days). Among those who experienced weight loss (median weight

loss 1.0kg), the median percentage decline from admission (baseline admission mean

weight 50.4kg) was 2.1% (range 0.2-16.6%). By the end of their hospitalisation, 52

participants (27%) experienced significant weight loss of >1% per week. Among this

group of participants (n=52), the median percentage decline was 3.6% (range 0.9-

16.6%).


224

7.3.1.2 Decline in arm anthropometric measures and SGA

Arm anthropometric measurements on both admission and discharge were available

for 88% of participants. Reduction of arm measurements (MAC, TSF, MAMC,

MAMA, CAMA) during hospitalisation occurred in 33-43% of the participants. The

median decline and median percentage decline in arm measurements during

hospitalisation are shown in Table 7-2. The greatest median percentage decline

relative to baseline was found using TSF (9.1%, range 2.6-35.3%), followed by

CAMA (4.9%, range 0.2-31.1%). The SGA was completed at both admission and

discharge for 99% of participants, of which 4% (11 participants) showed decline in

the SGA status. More participants were identified with decline in nutritional status

when the anthropometric criteria (27-44%) were used as assessment methods

compared to SGA (4%).


225

7.3.2 Factors associated with weight loss > 1% per week during

hospitalisation

Table 7-3 shows appetite, chewing, swallowing, clinical and nutritional characteristics

that are potential risk factors for weight loss during hospitalisation. Participants on

thickened fluids were more likely to experience weight loss >1% per week (p=0.03).

None of the other variables were found to be significantly (p<0.05) associated with

weight loss.

Of those with significant weight loss of >1% per week, 27% (37/139) were well-

malnourished and 28% (15/54) malnourished (p=0.86) as defined by admission SGA.

There were no statistically significant (p<0.05) associations between participants’

nutritional status on admission, regardless of malnutrition assessment method, and

risk of significant weight loss during hospitalisation. Both well-nourished and

malnourished patients were equally likely to experience significant weight loss during

hospitalisation.


226

Table 7-3: Association of appetite, chewing, swallowing, clinical and nutritional characteristics of older adults (>60 years) with weight loss >1% per week during acute hospitalisation [median LOS 9 days (range 2-50 days)]

Appetite, chewing, swallowing , clinical and nutritional characteristics

n Weight loss <1% per week

(n=141)

Weight loss >1% per week

(n=52)

p value

Âppetite Good Poor

191 86(62) 53(38)

31(60) 21(40)

0.868

^Dentition Good Poor

191 13(9)

126(91)

7(14) 45(86)

0.431

Swallowing impairment Yes No

191 8(6)

131(94)

5(10) 47(90)

0.346

^Modified Texture Diet Yes No

191 50(36) 89(64)

25(48) 27(52)

0.137

^Thickened fluid Yes No

191 2(1)

137(99)

4(8)

48(92)

0.027*

aDementia Yes No

193 45(32) 96(68)

14(27) 38(73)

0.598

bDepression Yes No

186 39(28) 98(72)

14(29) 35(71)

1.000

cSGA - Well-nourished Malnourished

193 102(72) 39(28)

37(71) 15(29)

0.859

dMNA- Well-nourished Malnourished

192 113(80) 28(20)

39(76) 12(24)

0.688

eBMI- Well-nourished Malnourished

192 112(79) 29(21)

39(76) 12(24)

0.692

fCAMA- Well-nourished Malnourished

192 110(78) 31(220

34(67) 17(33)

0.131

Abbreviations: SGA: Subjective Global Assessment; MNA: Mini Nutritional Assessment; BMI: body mass index; CAMA: corrected arm muscle area Results are expressed as n(%) for all variables. Only participants with weight measurements on admission and discharge were included. ^Self-reported by participants or caregivers. * Significantly different between participants with weight loss <1% per week and weight loss >1% per week, performed by Fisher Exact test, p<0.05 a Defined using DSM IV criteria354 by physician b Defined using a single question “Do you often feel sad or depressed?”355 c SGA: Well-nourished=SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; d MNA: Well-nourished= MNA score >17 (at risk & well-nourished), Malnourished=MNA score<17 (malnourished) 339; e BMI: Well-nourished=BMI>18.5kg/m2, Malnourished=BMI<18.5kg/m2 259; f CAMA: Well-nourished=CAMA>21.6cm2(male),CAMA >21.4cm2(female); Malnourished=CAMA<21.6cm2(male),CAMA<21.4cm2(female).286


227

7.3.3 Decline in nutritional status during hospital isation and

clinical outcomes

The clinical outcomes of participants who experienced >1% versus <1% of weight

loss per week during hospitalisation are presented in Table 7-4. Participants who had

>1% of weight loss per week during hospitalisation had a higher risk of discharge to

higher level care (OR 2.64, 95% CI [1.31-5.31], p<0.05), even after adjustment for

covariates (adjusted OR 2.48, 95% CI [1.12-5.48], p<0.05) and regardless of

admission nutritional status.

With both admission and discharge weight measured for only 69% of participants, it

would be useful to identify alternative nutrition measures to monitor change in

nutritional status over an admission. CAMA is able to detect changes related to

muscle atrophy286. This study has shown that CAMA (median percentage decline

4.9%) was able to detect the greatest loss of body muscle protein compared to other

arm measurements (except TSF which measures fat loss) and CAMA was easier to

obtain (88%) than weight (Table 7-2). Furthermore, it was discussed in Chapter 5

(section 5.4.1.1) that CAMA could be a potential measurement to be used in addition

or as an alternative to SGA to diagnose malnutrition due to its ability to predict 6-

month mortality. Therefore to determine the potential usefulness of CAMA as an

alternative parameter for monitoring nutritional status, the association between

decline in CAMA during hospitalisation and clinical outcomes were evaluated (Table

7-5). A decline in CAMA predicted longer LOS (unadjusted OR 3.54, 95% CI [5.06-

6.09]; adjusted OR 3.35, 95% CI [1.80-6.24], p<0.05) and increased risk of discharge

to higher level care (unadjusted OR 2.66, 95% CI [1.49-4.74]; adjusted OR 2.46, 95%

CI [1.27-4.70], p<0.05).


228

Table 7-4: Weight loss of >1% per week of hospitalisation among older adults (>60 years) and the associated clinical outcomes

Outcomes N (n)#

Weight loss <1% per week (n=141)

Weight loss >1% per week (n=52)

Odds Ratio

95% Confidence

Interval

p value‡ Adjusted∞ Odds Ratio

Adjusted 95%

Confidence Interval

Adjusted p value‡

LOS , days, mean+SD Median (range) > 11 days^ <11 days

193 (186)

10.8+7.8 8 (2-50) 75(36) 91(64)

10.9+4.5 10 (4-24) 25(48) 27(52)

NR+ 1.69

NR

0.89-3.21

NR† 0.112

NR+ 1.47

NR

0.71-3.03

0.710† 0.300


193 (186)

27(19) 114(81)

20(38) 32(62)

2.64*

1.31-5.31

0.006

2.48*

1.12-5.48

0.025


192 (185)

51(36) 89(64)

15(29) 37(71)

0.71

0.35-1.41

0.327

0.56

0.26-1.23

0.148


192 (185)

10(7)

130(93)

3(6)

49(94)

0.80

0.21-3.01

0.737

0.57

0.13-2.48

0.455


178 (171)

76.5+28.8

75.6+29.5

NR+

NR

NR†

NR+

NR

0.549†

Abbreviations: LOS: length of stay; MBI: modified Barthel Index; NR: not relevant. Results are expressed as n(%)or mean+SD # N=sample size in univariate analysis; n=sample size in multivariate analysis ^ LOS >/<11days based on the average LOS for the patients in GRM unit ∞Adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI †‡ Comparisons between participants with and without weight loss >1% per week, performed by linear regression (†) and logistic regression (‡) (reference group: participants with weight loss <1% per week) * Weight loss >1% per week significantly associated with outcome, p<0.05 + Β (unstandardised coefficient) from linear regression, participants with weight loss >1% per week not statistically different in LOS and 6-month MBI

score


229

Table 7-5: Decline in CAMA during hospitalisation among older adults (>60 years) and the associated clinical outcomes

Outcomes N (n)#

CAMA No change/increased

(n=154)

CAMA Decline (n=92)

Odds Ratio

95% Confidence

Interval


Adjusted 95%

Confidence Interval

Adjusted p value‡

LOS , days, mean+SD Median (range) > 11 days^ <11 days

246 (234)

9.6+6.4 8 (2-50) 47(31) 107(69)

14.8+8.9 12 (4-42) 56(62) 36(39)

5.25+ 3.54*

3.33-7.18 2.06-6.09

<0.001† <0.001

4.36+ 3.35*

2.57-6.16 1.80-6.24

<0.001† <0.001


246 (234)

30(19) 124(81)

36(39) 56(61)

2.66*

1.49-4.74

0.001

2.46*

1.27-4.70

0.007


243 (231)

56(37) 97(63)

32(36) 58(64)

0.96

0.56-1.64

0.870

0.97

0.52-1.80

0.916


245 (233)

16(10) 138(90)

10(11) 81(89)

1.07

0.46-2.46

0.883

0.91

0.35-2.41

0.851

6-month MBI, mean+SD 218 (207)

71.0+32.8

72.0+28.2

NR+ NR NR† NR+ NR 0.392†

Abbreviations: CAMA: corrected arm muscle area; LOS: length of stay; MBI: modified Barthel Index Results are expressed as n(%)or mean+SD # N=sample size in univariate analysis; n=sample size in multivariate analysis ^ LOS >/<11days based on the average LOS for the patients in GRM unit ∞Adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI †‡ Comparisons between participants with and without decline in CAMA, performed by linear regression (†) and logistic regression (‡) (reference group: participants with no change/increased CAMA) * Decline in CAMA significantly associated with outcomes, p<0.05 + Β (unstandardised coefficient) from linear regression, participants with decline in CAMA had significantly longer LOS, but 6-month MBI score not

statistically different.


230

7.3.4 Comparison of participants with admission wei ght

In Chapters 4 and 5, it was highlighted that participants without weight measurement

on admission (n=43) were more malnourished, had poorer functional status on

admission and higher 6-month mortality. Of those with admission weight

measurements (n=238), 45 (19%) did not have weight taken at discharge. Participants

with (n=193) and without (n=45) discharge weight measurement were compared for

differences in any of their characteristics, nutritional status or clinical outcomes.

Table 7-6 shows that amongst participants with admission weight (n=238), those who

did not have discharge weight had higher prevalence of malnutrition (47% vs 28%,

p<0.05) than those who had both weights recorded. They were not significantly

different in any other clinical characteristics (Table 7-6). Table 7-7 shows that the

absence of recorded discharge weight was significantly associated with higher risk of

discharge to higher level care (OR 2.03, 95% CI [1.02-4.07], p<0.05) and lower 6-

month MBI (Β-15.53, 95% CI [-25.91, -5.14], p<0.05). After adjustment for the

covariates, the absence of recorded discharge weight remained predictive of 6-month

MBI (Β-10.51, 95% CI [-18.49, -2.53], p<0.05) only.


231

Table 7-6: Comparison of participants’ characteristics and malnutrition

prevalence between those with and without weight measurements at discharge#

Discharge weight

available

(n=193)

Discharge weight

not available

(n=45)

p value‡

Age 81.0+7.7 80.7+8.5 0826†

Gender-Male

-Female

83(43)

110(57)

21(47)

24(53)

0.656

Race-Chinese

-Non-Chinese

163(85)

30(15)

34(76)

11(24)

0.154

Dementia -Yes

- No

59(31)

134(69)

17(38)

28(62)

0.350

Depression-Yes

- No

53(29)

133(71)

8(19)

34(81)

0.212

Number of prescribed drugs 4.6+2.9 4.5+3.3 0.838†

Severity of illness a 2.1+0.4 2.1+0.3 0.890†

Charlson Comorbidity Index b 5.5+1.4 5.6+1.1 0.573†

Admission MBI c 65.6+24.3 59.0+28.1 0.112†

SGA

Well-nourished

Malnourished

139(72)

54(28)

24(53)

21(47)

0.015

CAMA

Well-nourished

Malnourished

144(75)

48(25)

30(68)

14(32)

0.354

Abbreviations: SGA: Subjective Global Assessment; CAMA: corrected arm muscle area; MBI; Modified Barthel Index # Total sample only included those with available admission weight (n=238) Results in table are expressed as n(% of total) or mean+SD. †‡Comparison between participants with and without discharge weight was performed using independent samples t-test (†) and Chi-squared test (‡). SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; CAMA: Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female), Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286





Yen Peng LIM- Final Seminar Document 232

Table 7-7: Comparison of clinical outcomes between participants with and without discharge weight measurements#

Outcomes N (n)#

Discharge weight available

(n=193)

Discharge weight not available (n=45)

Odds Ratio

95% Confidence

Interval


Adjusted 95%

Confidence Interval

Adjusted p value‡


237 (227)

11.8+7.0 9 (2-50) 99(42) 138(58)

13.0+8.1 11(3-39) 24(56) 19(44)

NR+ 1.89

NR

0.98-3.65

NS† 0.059

NR 1.54

NR

0.75-3.19

0.172† 0.243


236 (226)

47(24) 146(76)

17(40) 26(60)

2.03*

1.02-4.07

0.045

1.87

0.87-4.02

0.111


234 (224)

66(35) 125(65)

16(37) 27(63)

1.12

0.57-2.23

0.742

1.09

0.51-2.35

0.820


235 (225)

13(7)

178(93)

5(11) 39(89)

1.76

0.59-5.21

0.311

1.83

0.57-5.90

0.309


217 (207)

76.3+28.9

60.7+33.7

-15.53+ -25.91, -5.14

0.004†

-10.51+ -18.49, -2.53

0.010†

Abbreviations: LOS: length of stay; MBI: modified Barthel Index; NS: Not significant; NR: Not relevant # Total sample only included those with available admission weight (n=238) Results are expressed as n(%) or mean+SD # N=sample size in univariate analysis; n=sample size in multivariate analysis ^ LOS >/<11days based on the average LOS for the patients in GRM unit ∞Adjusted for age, gender, race, dementia, depression, severity of illness, Charlsons Comorbidity Index, number of prescribed drugs and admission MBI †‡ Comparisons between participants with and without discharge weight, performed by linear regression (†) and logistic regression (‡) (reference group: participants with discharge weight) * Participants without discharge weight significantly associated with 6-month mortality with and without adjustment of covariates, p<0.05 + Β(unstandardised coefficient) from linear regression, participants without discharge weight had significantly longer LOS and lower 6-month MBI score


233

7.3.5 Comparison of participants with and without w eight change

data

Figure 7-1 shows the flowchart for the participants with and without weight

measurement on admission and discharge. A total of 88 participants did not have

weight change data; 45 participants had admission but no discharge weight, and 43

had no admission weight. When participants with (n=193) and without (n=88) weight

change data were compared, Table 7-8 shows that those without were significantly

more depressed (30% vs 18%, p<0.05) and had higher number of prescribed drugs

(4.9+2.9 vs 3.9+3.1, p<0.05). They were not significantly different in the clinical

outcomes as shown in Table 7-9.

Amongst those without weight change data (n=88), 50% had no change or increase in

CAMA, 34% had decline in CAMA, and 16% had incomplete CAMA data.

Comparing those participants with both weight change data and CAMA (n=183), 41%

of those with significant weight loss (>1% per week) were correspondingly identified

with decline in CAMA.


234

Figure 7-1: Flowchart of participants with and without weight measurement on

admission and discharge

All participants n=281

Admission weight n=238

No admission weight n=43^

Discharge weight n=193*

No discharge weight n=45^

Discharge weight n=17

No discharge weight n=26

Weight loss n=85

Weight loss >1% per week n=52

*Total participants with weight change data = 193 ^Total participants without weight change data = 88


235

Table 7-8: Comparison of participants’ characteristics and malnutrition prevalence between those with and without weight change data

Weight change data

available

(n=193)

Weight change data

not available

(n=88)

p value‡

Age 80.9+7.2 82.2+8.4 0.176†

Gender-Male

-Female

81(42)

112(58)

43(49)

45(51)

0.280

Race-Chinese

-Non-Chinese

161(83)

32(17)

73(83)

15(17)

0.923

Dementia -Yes

- No

63(33)

130(67)

31(35)

57(65)

0.670

Depression-Yes

- No

55(30)

129(70)

15(18)

69(82)

0.037

Number of prescribed drugs 4.9+2.9 3.9+3.1 0.014†

Severity of illness a 2.1+0.4 2.1+0.4 0.376†

Charlson Comorbidity Index b 5.5+1.4 5.4+1.4 0.549†

Admission MBI c 59.1+27.1 59.7+30.5 0.883†

SGA

Well-nourished

Malnourished

134(70)

57(30)

65(75)

22(25)

0.435

CAMA

Well-nourished

Malnourished

120(62)

73(38)

64(73)

24(27)

0.084

Abbreviations: SGA: Subjective Global Assessment; CAMA: corrected arm muscle area; MBI; Modified Barthel Index Results in table are expressed as n(% of total) or mean+SD. †‡Comparison between participants with and without weight change data during admission was performed using independent samples t-test(†) and Chi-squared test(‡). SGA: Well-nourished =SGA rating A (well-nourished), Malnourished=SGA rating B (moderately malnourished) and C (severely malnourished) 235; CAMA : Well-nourished=CAMA>21.6cm2(male), CAMA >21.4cm2(female), Malnourished=CAMA<21.6cm2(male), CAMA<21.4cm2(female).286





236

Table 7-9: Comparison of clinical outcomes between participants with and without weight change data

Outcomes N (n)#

Weight change data available

(n=193)

Weight change data not available (n=88)

Odds Ratio

95% Confidence

Interval

p value‡ Adjusted∞

Odds Ratio

Adjusted 95%

Confidence Interval

Adjusted p value‡


280 (267)

11.5+7.5 9 (2-42) 83(43) 109(57)

12.4+8.3 10(2-50) 40(46) 48(54)

NR+ 1.09

NR

0.66-1.82

NS† 0.728

NR 1.14

NR

0.64-2.04

0.300† 0.658


277 (264)

55(29) 135(71)

26(30) 61(70)

1.05

0.60-1.82

0.873

0.93

0.50-1.72

0.812


272 (259)

66(36) 120(64)

33(38) 53(62)

1.13

0.67-1.92

0.645

1.44

0.79-2.63

0.230


278 (265)

24(13) 167(87)

8(9)

79(91)

0.71

0.30-1.64

0.416

0.59

0.23-1.52

0.277


246 (234)

71.1+31.2

69.3+33.5

NR+ NR

NS†

NR+ NR

0.295†

Abbreviations: LOS: length of stay; MBI: modified Barthel Index; NS: Not significant; NR: Not relevant Results are expressed as n(%) or mean+SD # N=sample size in univariate analysis; n=sample size in multivariate analysis ^ LOS >/<11days based on the average LOS for the patients in GRM unit ∞Adjusted for age, gender, race, dementia, depression, severity of illness, Charlson Comorbidity Index, number of prescribed drugs and admission MBI †‡ Comparisons between participants with and without weight change data during admission, performed by linear regression (†) and logistic regression (‡) (reference group: participants with weight loss data) * Participants without weight change data during admission significantly associated with 6-month mortality with and without adjustment of covariates,

p<0.05 + Β(unstandardised coefficient) from linear regression, participants without weight change data during admission had significantly longer LOS and lower

6-month MBI score


237

7.3.6 Recognition of malnutrition and referral to d ietitian for

treatment

A total of 97 participants were malnourished as defined by SGA on admission. Figure

7-2 shows that 65% of these malnourished participants were not recognised by the

nurses as ‘at risk’ during routine screening. Of all the malnourished participants, 46%

were not seen by a dietitian. Among those seen by a dietitian for treatment of

malnutrition, 28 were referred by the nurses through the nutrition screening protocol,

and the remaining 24 by the doctors based on standard routine care in the hospital

(described in section 2.2.4.1).

Amongst the 52 participants who experienced weight loss of >1% per week during

admission, 38 were not seen by a dietitian. Of the 38 participants not seen, 33 were

identified by the nurses as ‘not at risk’ through routine admission screening, and 31

were defined by SGA as well-nourished on admission.


238

Figure 7-2: Flowchart of malnourished participants (defined by SGA) on

admission and the corresponding malnutrition risk routinely screened by

nursing staff and the subsequent referral to dietitian

Timeliness of dietitian referral

Amongst the 76 participants who were referred to a dietitian by doctors or nurses

based on standard routine care (refer to section 2.2.4.1), the median time for referral

was two days (range 0-17 days) after admission. The median time for referral for

malnourished (1 day, range 0-17 days; n=52) and well-nourished (2 days, range 0-12

days; n=24) participants on admission (defined by SGA) did not differ significantly

(p=0.36, p>0.05). Participants who had weight loss >1% per week (n=13) during

hospitalisation were referred to a dietitian at a median of 3 days (range 0-14 days)

after admission, compared to a median of 1.5 days (range 0-12 days) for those who

experienced <1% weight loss per week (n=28). This difference was also not

statistically significantly (p=0.35, p>0.05).

Malnourished n=97

Not at risk* n=61

At risk* n=34

Not screened n=2

Dietitian n=22^

No dietitian n=39

Dietitian n=28^

No dietitian n=6

Dietitian n=2 ̂

*Malnutrition risk routinely screened by nursing staff using the TTSH NST ^ Total malnourished participants seen by dietitian = 52 (54%)


239

7.4 Discussion

7.4.1 Weight loss during hospitalisation

Weight change data were only available for 69% of the 281 participants. Close to half

(44%) of this group experienced weight loss during hospitalisation and a quarter

(27%) had weight loss (>1% per week) that was clinically significant. Both the well-

nourished and malnourished participants (defined by SGA) on admission experienced

similar prevalence of weight loss during hospitalisation (45% versus 43%

respectively, p=1.00) and had similar magnitude of weight loss (median 1.2kg and

0.9kg, respectively, p=0.84).

Comparison with other studies using weight loss as the criterion

Relatively few studies have monitored the changes in nutritional status of patients

during an admission (refer to Table 1-8). Only three studies59, 69, 137 reported using

weight loss as the criterion to determine the nutritional decline in hospital patients.

Gariballa et al (1998)69, McWhirter and Pennington (1994)59, and Corish et al

(2000)137 used weight loss as the criterion to determine decline in nutritional status in

their studies among both elderly (n=201, mean age 78 years)69 and general

hospitalised patients (n=500, age >16 years59; n=594, age 16-64 years137) in the UK.

Only 22-34% of the patients admitted were followed-up with their weight status upon

discharge in these three studies compared to 69% in the present study.

The proportion of participants who experienced weight loss (44%) in the present

study was close to the lower range reported in these three UK studies (43-75%). The

frequency of weight loss (44%) was generally lower than that reported by Gariballa et

al69 (66%). The mean percentage weight loss during hospitalisation from baseline


240

admission weight reported in these three studies ranged from 1.8%69 (in four weeks)

to 5.4%59 (duration not reported) compared to 2.1% (median 9 days) in the present

study.

The length of hospital stay, participants’ age and diagnoses are all likely to have an

impact on the weight change over an admission, but were not taken into consideration

in these three studies. Only Corish et al137 reported the median LOS (12 days),

whereas Gariballa et al69 repeated the weight measures after two weeks and four

weeks of admission instead of at discharge. McWhirter and Pennington59 did not

report the average LOS, however did report that weight change data were available

for 22% of participants. In the present study, when the percentage weight loss was

adjusted for the LOS (median 9 days), only 27% of participants showed weight loss

>1% per week that was clinically significant. This was much lower than that reported

in the three UK studies (43-75%). This was not surprising as the present study

included more participants regardless of their LOS, excluded participants who were

critically and terminally ill, and the weight loss criterion was more definitive.

Relationship with nutritional status on admission

Three studies59, 88, 137 compared the prevalence of weight loss during admission

between well-nourished and malnourished adult patients. Braunschweig et al (2000)88

monitored the nutritional status of 404 general hospitalised adults in the US who were

admitted for at least seven days (mean age 54 years, average LOS 17 days) based on

SGA and weight loss of any quantity (not defined in study). They showed that the

decline was more prevalent among well-nourished patients (38%) than in moderately

(20%) or severely malnourished patients (33%). Likewise, Corish et al (2000)137 in


241

their study of 594 adult patients (16-64 years) also showed that normal weight

patients (66%) were more likely to experience weight loss than malnourished patients

(48%). In contrast, McWhirter and Pennington59 reported that more malnourished

patients (75%) experienced weight loss than those who were well-nourished (39%) in

their study of 500 adult patients (>16 years). These results were different from the

present study where the prevalence of weight loss was similar regardless of admission

nutritional status (well-nourished 45% vs malnourished 43%).

A plausible explanation for the difference between the present study and the studies

by Braunschweig et al88 and Corish et al137, is that older patients in the present study

are more vulnerable to weight loss regardless of their nutritional status on admission.

Another potential factor could be differences in nutrition policy and interventions.

However the details for dietitian referral and treatment were not described in both the

studies88,137. The present study reported that 73% of participants with weight loss >1%

per week during admission were not seen by a dietitian. The referral to a dietitian was

also made later (median 3 days after admission) for those who experienced significant

weight loss compared to those who did not (median 1.5 days after admission). The

effectiveness of nutrition interventions is also important in influencing the change in

nutritional status; however this was not evaluated in the present study. Moreover, the

LOS (median 9 days) in the study was relatively short for any potential benefits of

nutrition intervention to be evident and affect weight measures. Lastly, the studies

compared here were all conducted more than 10 years ago when awareness of

malnutrition might have been poorer amongst healthcare professionals, resulting in

the higher prevalence of nutritional decline than the present study.


242

Challenges of using weight loss as a criterion

Weight loss of >5% in 30 days or >10% over six months have been considered

clinically significant26, 235, 236 and associated with poor clinical outcomes233, 237.

However due to the relatively short hospital stay (average LOS of GRM patients is 11

days) of participants in the present study, weight loss >1% per week was considered

clinically significant236, 366, and used to define decline in nutritional status (refer to

Chapter 2 section 2.2.4.2). However, potential challenges with the use of weight to

monitor changes in nutritional status over a hospital admission were discovered. .

Firstly, it was difficult to weigh acutely ill hospitalised elderly patients both on

admission and upon discharge. This is shown to be true in the present study (69%

success rate) and in the literature113, 120, 203. The incomplete weight change data would

potentially underestimate the prevalence of weight loss during admission. Secondly,

weight changes during acute illness could be reflective of changes in fluid status and

balance, such as dehydration or fluid retention. It is possible that the extent of

nutrition-related weight loss might have been underestimated among patients who had

been admitted with dehydration in the present study.

The median crude weight loss in the present study was relatively small (1.0kg, range

0.1-6.8kg) compared to the diurnal variation of body weight due to fluid retention,

which can be as much as 2 kg233. Although the absolute weight loss was relatively

small, this was equivalent to median percentage weight loss of 2.1% over nine days

(median 1.5% per week) in the hospitalised elderly (mean baseline admission weight

50kg). It has been suggested that 5% loss of usual weight within six months increases

the risk of nutrition-related complications in elderly patients268. Therefore, the extent

of weight loss shown in the present study (2.1%) could signify a problem that would


243

require closer attention by the healthcare team. Despite the difficulties in obtaining

weight loss data in clinical practice it is still an important parameter to monitor in

hospitalised patients as it is quick, inexpensive and can be measured by anyone.

Factors associated with weight loss

The factors contributing to weight loss caused by loss of fat and muscle mass shown

in the present study are unclear. Being on thickened fluids at admission was the only

factor associated with weight loss. No other baseline characteristics, including

nutritional status on admission, have been identified in this association. It is plausible

that factors during hospitalisation are more likely to influence the change in

nutritional status. Routine hospital practices such as withholding meals due to

diagnostic procedures, delay in initiating nutrition support, and little emphasis on

nutrition education among nursing and medical staff, could adversely affect patient

nutritional status58,26. Inadequate dietary intake during hospitalisation has been

reported in 21% of elderly patients27 and may be the main contributor of weight loss

observed in the present study.

Since older adults are vulnerable to nutritional decline, they are equally at risk of

experiencing weight loss during hospitalisation regardless of their nutritional status on

admission. Therefore all hospitalised older adults should be closely monitored to

reduce the risk of decline in nutritional status. However, since weight was available

for only two-thirds of participants, an alternative parameter must be sought for those

whom weight measure is not possible (e.g. bedbound patients).


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7.4.2 Decline in arm anthropometry during hospitali sation All arm anthropometric measures (on admission and upon discharge) were available

for 88% participants compared to only 69% for weight. All mean arm anthropometric

measures, except mean CAMA, were significantly lower upon discharge compared to

admission (median LOS 9 days) (Table 7-1). As CAMA is a derivative measure for

lean body mass from MAC and TSF, its value is dependent on the corresponding

changes in both measurements. However among patients who experienced decline in

their anthropometric measurements, CAMA was able to detect the change with the

highest median percentage change (4.9%) from baseline compared to the other arm

anthropometric indicators for somatic protein reserves (i.e. MAC, MAMC and

MAMA; 2.1-4.5%). The present study demonstrated that CAMA was best able to

detect loss of lean body mass compared to the other arm measurements, as it is a

measure only of lean muscle mass and excludes non-muscle mass such as bone and

fat mass286.

Among all the anthropometric measurements taken at admission and discharge, TSF

(which measures the subcutaneous fat and is an indirect measure of total body fat)

showed the greatest mean percentage decline (9.1%). This is consistent with the

metabolic response during starvation (without stress) when subcutaneous fat stores are

preferentially metabolised while the loss of muscle is much slower in the adaptive

response to preserve lean body mass377. The different responses of the body fat and

muscle changes that reflected the nutritional decline of the older adults in the present

study could imply that the changes in nutritional status was mainly related to

starvation, rather than stress hypermetabolism during hospitalisation377.


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From the literature reviewed, no other studies have looked at the change in CAMA

and TSF during hospitalisation and compared the magnitude of decline with these

anthropometric measures.

7.4.3 Decline in nutritional status during hospital isation and

clinical outcomes

Decline in nutritional status, defined by weight loss >1% per week of admission, was

independently associated with two and a half times greater risk of discharge to higher

level care. In addition, participants with decline in CAMA were three times more

likely to have longer LOS (>11 days). From the literature reviewed, all the studies that

had monitored the change in nutritional status of patients during hospitalisation (refer

to section 1.5.5 and Table 1-8) did not analyse the impact of its decline on clinical

outcomes. The present study demonstrated that nutritional deterioration during

admission predicts discharge to higher level care and longer LOS (>11days) (refer to

section 7.3.3, and Tables 7-4 & 7-5).

One possible reason for weight loss >1% per week to be predictive of less outcomes

than CAMA could be related to type two error since the sample size was smaller for

the weight loss group. The lack of influence from the decline in nutritional status on

other clinical outcomes could potentially be attributed by the relatively short LOS of

the present study sample (n=193, median 9 days).

Data from the present study suggest that any decline in CAMA is as important as

weight loss of >1% per week due to its associations with clinical outcomes. Since

weight loss may reflect either fluid, fat or muscle loss, or all of them in combination,


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a cut-off i.e. >1% per week or >10% in 6 months must be applied to define its clinical

significance. Any decline in CAMA may be more indicative than weight loss in

defining nutritional decline as the former indirectly measures muscle loss. CAMA has

been shown to be predictive of clinical outcomes in other studies229, 277, 287, but none

had evaluated the association of reduced CAMA with outcomes among hospitalised

older adults.

7.4.4 Absence of recorded admission or discharge we ight, and

association with nutritional status and clinical ou tcomes

Participants who had their admission weight taken, but not their discharge weight

(n=45) had higher malnutrition rates (defined by SGA) on admission and had lower 6-

month MBI (refer to section 7.3.4). This was similarly reported in those without

admission weight (n=43, previously discussed in sections 4.5.5 and 5.4.5). It is

therefore clinically important for patients without admission or discharge weight to be

assessed and monitored using alternative methods so that they can be identified early

for further assessment and receive any potential interventions.

Participants who did not have weight change data (n=88) were not significantly

different in their nutritional status and clinical outcomes to those with weight change

data (n=193). Considering those without admission weight (n=43) and those with

admission but without discharge weight (n=45) had poorer nutritional status and

clinical outcomes, it may appear unusual that the participants without weight change

data did not present likewise. This finding is hard to explain, but one plausible reason

could be the varying changes in the clinical and nutritional characteristics during

admission that might have influenced the outcomes observed.


247

7.4.5 Alternative measure for monitoring nutritiona l status during

admission

Weight changes during admission could be reflective of changes in hydration status

rather than specific changes in lean body mass or nutritional status. It is not possible

to isolate the loss of fluid, fat or lean mass from weight loss alone. However other

nutrition parameters also pose limitations.

Composite measures such as the MNA may not be suitable as many of the questions

relate to characteristics associated with acute illness and tend to improve when

patient’s clinical conditions improve. This is the main reason for the MNA not being a

discharge measurement in this study. The SGA was shown in this study to be less

useful than anthropometric measures for short hospital stay (median LOS 9 days) as it

only detected 4% of participants with change in the SGA status during admission. The

SGA may be more useful in detecting nutritional changes over a longer duration97.

CAMA, which indirectly measures lean body mass, was shown to be a useful

alternative measure for monitoring nutrition status. CAMA can be easier to measure

in patients whom weight cannot be obtained i.e. those who are bedbound. This was

reflected in the present study by its higher completion rate than weight (88% vs 69%),

and its ability to assess 84% of those without weight change measurements. Although

CAMA was only able to identify 41% of those with significant weight loss, it was

best suited to detect decline in lean muscle mass. Therefore CAMA may be an

alternative measure to monitor change in nutritional status during a hospital admission

when weight is not available on admission. CAMA can also be considered as an

additional routine measurement when weight is available.


248

7.4.6 Recognition of malnutrition and referral to d ietitian for

treatment

Only one-third of the malnourished participants were recognised as at risk of

malnutrition by the nurses during routine care. This low recognition was associated

with the poor sensitivity of the TTSH NST when completed by the nurses, as

previously discussed in Chapter 6 (refer to section 6.4.1). However more than half

(54%) of the malnourished participants were subsequently referred by the nurses or

doctors and were seen by a dietitian during admission. The level of referral in the

present study was higher than that reported in an earlier study conducted in 2004 at

the same hospital among younger medical and surgical patients (16% of malnourished

participants seen by a dietitian)60.

The relatively higher referral rate of malnourished patients could be related to the

implementation of a hospital-wide nutrition screening protocol170 after the previous

malnutrition study in the same hospital was conducted60. Improvements in appropriate

dietetic referrals (from 56% to 71% over five years) for malnourished patients was

achieved with the introduction of a nutrition screening tool in UK hospitals135. The

higher level of referral to a dietitian in the present study could also be influenced by

the heightened awareness of this ongoing study by the staff and facilitated by the

increased presence of the dietitian (YP Lim) in the wards. Close to two-thirds of the

referrals to the dietitian (63%) were actually by doctors. This highlights the important

complementary role of the doctor in helping to identify patients at risk of malnutrition

who require nutritional intervention, rather than relying solely on the hospital

nutrition screening policy.


249

Almost three-quarters of the participants (73%) presenting with significant weight

loss (>1% per week) during admission were not seen by a dietitian. This was likely

contributed by a large proportion (87%) of these participants being screened on

admission as ‘not at risk’ by the nursing staff using the TTSH NST, and also 82%

were defined by SGA as well-nourished on admission. This finding highlighted the

inadequacy of the current nutrition policy to identify patients who are at risk of

nutritional decline during admission.

Most studies have focused on the nutrition screening protocol upon hospital

admission. There is relatively weaker focus on the nutrition protocol for

identification, prevention, and treatment of decline in nutritional status during

hospitalisation. This continual assessment and monitoring is particularly important in

older adults who are susceptible to nutritional deterioration during hospitalisation as

shown in this study (refer to section 7.3.1).


250

7.5 Conclusion

There was a decline in anthropometric measurements from admission to discharge

(median LOS 9 days) in hospitalised older adults. Twenty-seven per cent of

hospitalised older adults experienced a decline in nutritional status defined by weight

loss of >1% per week. Both well-nourished and malnourished participants were

equally prone to weight loss during admission. This level of nutritional decline in the

present study was lower than that reported in other studies. It could be related to the

shorter LOS and the use of different assessment methods to determine change in

nutritional status.

CAMA best detected decline in lean body mass (4.9%) compared to the other

anthropometric measures, and was more easily measured (88%) than weight loss

(69%). Decline in CAMA was comparable to weight loss (>1% per week) in

predicting clinical outcomes. CAMA was predictive of both LOS (>11 days) and

discharge to higher level care, whilst weight loss >1% per week was only associated

with the latter. This evidence suggests that CAMA could potentially be a useful

alternative nutrition parameter for monitoring nutritional status of older adults during

hospital admission when weight measurement is not available on admission.

The referral rate of malnourished older patients to a dietitian for treatment in in Tan

Tock Seng Hospital (54% of malnourished participants) was better than other studies.

However, despite the existence of a hospital nutrition screening policy, the level of

malnutrition recognition and referral to a dietitian remained unsatisfactory. Of the

malnourished patients on admission identified by SGA, two-thirds (65%) failed to be

identified through screening and nearly half (46%) were not referred to and seen by a


251

dietitian. Most of the patients with a decline in nutritional status during admission

(73%) were also not seen by a dietitian.

The present study has shown that malnutrition on admission and decline in nutritional

status are common in Singapore hospitalised older adults. However, the existing

routine care and nutrition policy are inadequate to address these issues. It is therefore

important for appropriate nutrition policies and protocols to be implemented so that

all hospitalised older adults are monitored closely during hospitalisation. Early

referrals to a dietitian for nutritional intervention could be made to minimise the

nutritional decline during hospital admission and follow-up nutrition monitoring and

evaluation post-discharge could be planned.


252

8 Study significance, strengths, limitations and

recommendations

8.1 Overview of study significance and outcomes Older adults, especially those in acute care settings, are vulnerable to malnutrition.

The high prevalence of malnutrition has been recognised as an important problem in

hospitalised older adults due to the frequent associations with adverse clinical

consequences. However it was not clear if these relationships were influenced by

other confounders such as cognition, comorbidity and functional status, as few studies

had adjusted for these factors. Acutely ill older adults are also exceptionally prone to

nutritional decline but limited reports have studied this change during hospitalisation

and its impact on clinical outcomes. Despite the high prevalence, malnutrition

remains under-recognised and under-treated. Although many screening and

assessment tools are available, there is no universally accepted method for defining

malnutrition risk and nutritional status. Evidence on malnutrition is lacking in the

rapidly ageing Singapore population which has unique socio-demographic

characteristics and no tools for nutritional evaluation have been validated in

Singapore.

This study was therefore planned to characterise the hospitalised elderly in a

Singapore acute hospital and to describe the extent and impact of admission

malnutrition. It also aimed to identify suitable methods for nutritional screening and

assessment, and to examine changes in nutritional status during admission and the

impact on clinical outcomes.


253

This is the first study of its kind in older Singaporean adults. On admission

malnutrition prevalence was 35% defined using the SGA. Decline in nutritional status

during admission was prevalent (27%) regardless of the admission nutritional status.

The SGA was the best assessment tool as it predicted a two- to three-fold increased

risk of LOS>11days, 3-months readmission and 6-months mortality, independent of

patient, cognitive, comorbidity, and functional covariates. The TTSH NST was the

best screening tool with good diagnostic and prognostic performance (key findings

described further in section 8.2)

The key strengths of this study lie in the longitudinal design with adjustment for a

comprehensive range of covariates in the evaluation of the relationship between

nutritional status and clinical outcomes; the consecutive sampling framework with a

good sample size and excellent retention at 6months; validation of nutrition screening

and assessment tools in the same sample population; and high reliability of data with

single, blinded assessors (described further in section 8.3). Although there were

limitations in the study (described further in section 8.4), they too contributed

important findings to be considered when designing future studies.

This thesis contributed towards a better understanding and knowledge of the

characteristics, nutrition and health risks of Singaporean older adults in an acute care

setting. It provided an evidence base for the clinical application of nutrition screening

and assessment tools (described further in section 8.5.1). This can assist stakeholders

to appropriately plan, develop and evaluate nutrition-related services and programs,

with the aim of improving the care and clinical outcomes. Areas for future research

were also suggested (described further in section 8.5.2).


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8.2 Summary of key findings

8.2.1 Research Question 1: What are the sociodemogr aphic

indicators, nutritional, appetite, chewing, swallow ing status,

and clinical characteristics, according to age, of older adults

upon hospital admission?

This study comprised 281 participants from three geriatric wards in a Singapore acute

hospital, with a mean age (+SD) of 81.3 (+7.6) years. They were predominantly

Chinese (83%), and community-dwellers (97%); representative of the population.

Cognitive impairment was prevalent in 33% of the participants, 62% reported weight

loss, 42% reported poor appetite prior to hospital admission, 90% reported poor

dentition and 40% were on a modified texture diet.

8.2.2 Research Question 2: What is the prevalence o f malnutrition,

as defined by four different nutrition assessment m ethods

(SGA, MNA, BMI, CAMA), of older adults upon hospita l

admission and its associated characteristics?

The prevalence of malnutrition on admission was shown to be high (23-35%) among

older adults in a Singapore acute care hospital. The prevalence varied with the

nutrition assessment methods used, with the SGA returning the highest malnutrition

prevalence at 35% and the MNA returning the lowest at 23%. The highest prevalence

(SGA 40%) was shown among the oldest old aged >85 years. Among the four

nutrition assessment methods used in this study, CAMA and the SGA had the highest

completion rates (99% and 100%) and were more likely to be useful in determining

malnutrition prevalence.


255

Swallowing impairment, poor appetite, the need for modified texture diet, dementia,

depression, poor functional status pre-morbid and on admission, and low serum album

were characteristics associated with malnutrition on admission in the study sample.

These clinical characteristics confirmed that they are important risk factors for

developing malnutrition.

8.2.3 Research Question 3: What are the predictive abilities of the

four nutrition assessment methods, and which one is most

suitable for use in hospitalised older adults?

The SGA-defined malnutrition remained as a significant predictor for the most

number of clinical outcomes: LOS>11days (OR 2.12, 95% CI [1.17-3.83]);

readmission at 3-month (OR 1.90, 95% CI [1.05-3.42]); and mortality at 6-month (OR

3.04, 95% CI [1.28-7.18]), independent of the influences from a comprehensive range

of covariates (age, gender, race, dementia, depression, severity of illness, Charlson

Comorbidity Index, number of prescribed drugs and admission MBI). Besides the

highest completion rate for the SGA (100%) and its ability to predict the most number

of clinical outcomes at univariate (4 out of 5) and multivariate analyses (3 out of 5),

the diagnostic performance of the SGA (in a logistic regression model) for clinical

outcomes was also slightly superior to the other nutrition assessment methods (mean

AUC 0.72). These findings supported the selection of the SGA as the most

appropriate reference standard for malnutrition diagnosis for the study population and

setting.


256

8.2.4 Research Question 4: Which of the four nutrit ion screening

tools has the best validity in predicting malnutrit ion when

compared against the identified malnutrition assess ment

method (from Research Question 3) in the study, and hence

most suitable for use in hospitalised older adults?

The TTSH NST (when completed by a dietetic technician) showed the best diagnostic

performance against the SGA, compared to the other three nutrition screening tools. It

achieved 100% completion rate and showed the highest AUC (0.865) and the best

balance between sensitivity (84%), specificity (79%), positive predictive value (68%)

and negative predictive value (90%). In addition, the TTSH NST also demonstrated

good prognostic performance for LOS >11days even after adjustment for a

comprehensive range of covariates (OR 1.89, 95% CI [1.05-3.35]). It remained a valid

tool even among the oldest old hospitalised patients (aged >85 years) at the same

optimal cut-off score of 4. From these results, the TTSH NST was selected as the

most appropriate nutrition screening tool to be applied to hospitalised older adults in

Singapore.

8.2.5 Research Question 5: What are the changes in

anthropometric measurements and nutritional status of older

adults during hospital admission and the impact of reduced

nutritional status on clinical outcomes?

Anthropometric measurements (weight, MAC, TSF, MAMC, MAMA) decreased

from admission to discharge (median LOS 9 days) in hospitalised older adults in this

study. Overall, 44% of hospitalised older adults in this study experienced weight loss

during admission, and the weight loss was significant (>1% per week) in 61% of them


257

(27% of overall participants). Well-nourished (45%) and malnourished (43%)

hospitalised older adults on admission were equally prone to experiencing decline in

nutritional status (weight loss >1% per week) during hospitalisation. However, weight

loss (from admission to discharge) was only available for 69% of participants. CAMA

was more easily performed (88%) and shown to be the most sensitive anthropometric

measure to detect muscle atrophy (4.9%) in the study sample. Decline in CAMA was

shown to be predictive of more clinical outcomes (LOS >11 days OR 3.35, 95% CI

[1.80-6.24]; discharge higher level care OR 2.46, 95% CI [1.27-4.70]) than weight

loss >1% per week (discharge higher level care OR 2.48, 95% CI [1.12-5.48]) after

adjustment for covariates. Results from this study suggest that CAMA could

potentially be a useful nutrition parameter for monitoring nutritional status during

hospital admission when weight measurement is not available.

The level of malnutrition recognition and dietitian referral was unsatisfactory in this

study sample despite the existence of a hospital nutrition screening policy. Amongst

the malnourished participants identified, only 36% were recognised as at risk of

malnutrition during routine nutrition screening conducted by the nurses. Overall, 54%

of malnourished older adults were referred to and seen by a dietitian during hospital

admission based on standard routine care. More severely malnourished (88%)

participants were referred to a dietitian compared to moderately malnourished (46%)

ones. Most of the participants with decline in nutritional status (weight loss >1% per

week) during admission (73%) were also never seen by a dietitian. Fifty-eight per cent

of participants who were seen by a dietitian on admission were not reviewed again by

a dietitian prior to hospital discharge.


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8.3 Strengths and contributions to new knowledge

8.3.1 Longitudinal study with adjustment for covari ates

One of the main strengths of this study was the study design. It was a prospective

longitudinal study on the prevalence and impact of malnutrition on clinical outcomes

for up to 6 months, with adjustment for a comprehensive range of covariates.

Compared to the 14 longitudinal studies (n=50-1145) conducted among hospitalised

elderly and followed up beyond three months post-discharge (refer to Table 1-10), the

present study sample (n=281) was larger than six of these studies. Only two published

studies have been conducted in Singapore on malnutrition and clinical outcomes. One

of the studies conducted among general medical and surgical patients in an acute

hospital (n=658, mean age 56 years) showed malnutrition (defined by SGA) on

admission adversely impacted on clinical outcomes such as. in-hospital mortality and

LOS60. However, this association was only adjusted for age and admitting medical

discipline and patients were not followed-up post discharge. Although the other study

conducted in a Singapore nursing home (n=154, mean age 77 years) showed that

malnutrition (BMI<18.5kg/m2) was associated with increased mortality over two

years with adjustment for covariates (age, gender, functional status, comorbidity and

prior nutrition intervention), it only studied the impact of malnutrition on one clinical

outcome (mortality).

In addition, the change in nutritional status of the participants during hospitalisation

was also monitored in this study. Previously only a few studies had reported this

change (refer to Table 1-8). None of those studies had reported the impact of the

decline in nutritional status on the clinical outcomes. The present study was able to

contribute clinically valuable data on the prevalence and extent of the decline in


259

nutritional status of geriatric patients during admission. At the same time, it provided

new and useful results on the impact of the nutritional decline on clinical outcomes

with adjustment for a wide range of covariates.

The relationships between malnutrition with clinical outcomes shown in the previous

studies have often been criticised that they could have been confounded by patients’

cognition, functional status, severity of illness and comorbidities as multiple logistic

regression analysis were not performed. In fact, there were only nine studies (see

Table 1-11) which adjusted for covariates in their study analysis and only seven were

conducted among hospitalised older adults. Most of these studies adjusted for fewer

covariates (ranged from two to six) than the present study did and only one study94

included similar covariates as the present study. Therefore in the present study, a very

comprehensive range of nine covariates were included to address the limitations from

the previous studies. Although the relationships were attenuated after the adjustments,

malnutrition remained predictive of the adverse outcomes e.g. 6-months mortality.

The findings of this study have established important evidence that malnutrition on

admission to hospital adversely affects patient clinical outcomes post-discharge,

regardless of their age, gender, medical condition, cognitive function, psychological,

and functional status on admission. These results strengthen the independent

association between malnutrition and clinical outcomes and have clearly highlighted

the importance of malnutrition in older adults. This is the only study to date that has

studied the prevalence and impact of malnutrition on clinical outcomes among

hospitalised older adults in Singapore.


260

8.3.2 Large data set in a hospitalised elderly popu lation with low

drop-out rate

The other major strength of this study is the relatively large study sample. The sample

is representative of the hospitalised older population as a consecutive sampling

framework was used for recruitment. In addition, patients who were cognitively

impaired were included. Cognitive impairment is prevalent among hospitalised older

adults and is an important risk factor for malnutrition31, 37. Therefore it is important

that they are included in the recruitment for more accurate determination of

malnutrition prevalence. Many previous studies have excluded the cognitively

impaired patients and that might have underestimated the actual malnutrition

prevalence378.

In addition to the large study sample, there was a low drop-out rate (1%) during the 6

months follow-up and. most participants were included in the analysis. However, the

completion rate for selected variables (BMI, MNA) was only 84%, resulting in the

exclusion of participants for analysis involving these data variables. This was still

better than other studies with completion rates ranging 56%-66% using the same

nutrition assessment methods203, 379.

8.3.3 Validation and comparison of nutrition assess ment methods

against clinical outcomes

Malnutrition has been widely reported to be associated with and predictive of adverse

clinical outcomes in hospitalised older adults7. It is important that malnutrition is

diagnosed appropriately and accurately upon hospital admission to identify patients

with risk of developing poor clinical outcomes. Nutrition interventions can then be


261

initiated with the aim to reduce the risk of adverse outcomes. A valid nutrition

assessment method should be able to adequately predict the associated outcomes.

A unique feature and strength of this study was the selection of a reference standard

for defining malnutrition. The different nutrition assessment indices and tools were

validated against the clinical outcomes with adjustment for a comprehensive range of

covariates. Many different methods to identify malnutrition have been used in other

studies. However none has adopted the approach of evaluating these methods against

clinical outcomes to identify a reference standard for malnutrition assessment.

Only a few studies113, 129, 203, 380 conducted in hospitalised older adults had compared

the different nutrition assessment methods within the same study. Only two of these

studies113, 203 specifically evaluated the methods against clinical outcomes. Therefore

this study added new knowledge to the evaluation and validation of nutrition

assessment methods by comparing composite nutritional assessments (SGA and

MNA) and anthropometric measurements (BMI and CAMA) against clinical

outcomes.

In addition, most studies relating to nutritional assessment of hospitalised older adults

were conducted among non-Asian populations. The present study is one of the very

few studies done in an Asian population, represented predominantly by Chinese older

adults from an acute care setting. It has been shown that the body composition of

Asian and Singaporean population differed from the Caucasian population, and the

BMI cut-offs for predicting cardiovascular risks are different between the two

groups262, 264. Therefore the relationships between malnutrition and clinical outcomes


262

and the application of the nutrition assessment methods may differ between

populations.

This study was therefore able to identify some of these similarities and differences.

For example, certain nutrition assessment methods such as SGA are not as population

specific and sensitive, and can be applied to most populations since the results are

consistent with other studies94, 113. Other assessment methods such as BMI may need

different cut-offs for application in different populations (refer to section 8.4.5 for

more detailed discussion) since results are still inconsistent from existing literature in

the elderly population146, 242, 265.

8.3.4 Validation and comparison of nutritional scre ening tools

against SGA

Besides comparing four nutrition assessment methods, this study also evaluated four

nutritional screening tools against the reference standard for malnutrition

classification (SGA), which has been specifically validated for hospitalised older

adults in Singapore (refer to section 6.4.3). The SGA was also frequently used as a

reference standard for other nutritional screening tool validation studies184, 381, 382.

Although several reviews on nutritional screening tools are available183, 383, 384, only

one study224 had specifically compared three different nutritional screening tools

(MUST, NRS 2002, Nutritional Risk Index) against the SGA. Most validation studies

on nutritional screening tools evaluated each tool individually against varying

reference standards. These selected standards are usually based on the literature

reviewed by the authors.


263

By first establishing the reference standard for nutrition assessment in the present

study using a validation approach in the same population, a more accurate validation

and comparison between the screening tools could be performed. The selection of an

appropriate malnutrition reference standard for the validation of screening tools is

important as it affects the diagnostic performance of the tools.

This study provides useful comparisons on the performances of the various screening

tools when applied to the Singapore hospitalised older adults. This is an important

addition to the limited research knowledge on the validation of screening tools in

hospitalised older adults. There was only one other study224 which had similarly done

so but in younger hospitalised adults comparing different set of screening tools. When

compared against a common reference standard (SGA), the present study showed that

a locally developed screening tool (TTSH NST) performed best when it was

administered by a dietetic technician. The relatively poorer performance of the other

well-established screening tools e.g. the MNA-SF in the study population compared

to other studies, demonstrated that the performance of screening tools can differ

between population groups and validation reference standards.

8.3.5 High reliability of data with blinded single assessor

In order to minimise inter-rater variability and assessor bias, a single dietetic

technician performed the screening with the participant or family. At a second and

separate interview, a single dietitian performed the nutrition assessments, blinded to

the screening outcomes. There was a high reliability of the data collected as the

nutritional screening and nutritional assessments were each completed by a single but

different assessor353. This was important as the screening outcomes could potentially


264

influence the nutritional assessments performed by the dietitian and vice versa.

Previous studies which compared nutritional assessment and screening tools have not

indicated if the assessors conducted the assessment and screening independently or if

they were blinded to the results203, 224. Although more than one assessor was involved

in these studies203, 224, it appeared that one assessor completed all the assessment and

screening for each participant.

Although it may appear that the ratings of the SGA and MNA could be biased if the

same assessor (dietitian) completed the assessment, this was unlikely to occur. The

SGA, a more subjective assessment tool without a scoring system, is more likely to be

influenced by the outcomes of other assessment tools as it is based on the assessor’s

clinical judgment. Therefore to minimise the influence from the MNA, the SGA was

completed prior to the computation of the total MNA score. The MNA is an objective

assessment tool with specific scoring of responses and hence its total scoring is

unlikely to be influenced by knowing the rating of the SGA. This approach was

similarly reported by Christensson et al (2002)129 in their study to evaluate the validity

of the SGA and MNA among 261 elderly residents in long-term institutionalized care

in Sweden, where a single assessor performed both the SGA and MNA.

If two different assessors were involved to complete the SGA and MNA

independently, the participants would be interviewed and assessed repeatedly

resulting in respondent burden, and inter-rater variability could be introduced.

Likewise, completing the four objective screening tools by a single assessor (dietetic

technician) would not influence the objective scoring of the screening tools. This also

reduces the respondent burden. However, having a single assessor could possibly lead


265

to generalisation of responses from the participants and potentially lead to bias. The

dietetic technician was trained to assess responses individually in the study to

minimise generalisation of responses from occurring.

8.3.6 Older adults awareness of their weight and we ight history

Self-reported percentage weight loss (of usual weight) prior to hospital admission has

been shown to be a valid nutritional screening and assessment variable for identifying

malnutrition on admission and is predictive of adverse clinical outcomes233, 235, 239, 242.

However, one important finding from this study was the high unavailability of this

weight loss information. Most participants (73%) were unaware of their usual body

weight, and of those who reported weight loss almost all (98%) were unable to

quantify the amount of loss. This situation is common as weighing of patients is not a

routine procedure across all care settings in Singapore. Many older adults do not

weigh themselves regularly or they cannot recall their weights. Sometimes, weighing

is difficult to perform especially for patients who have difficulty standing and a chair

scale is not available. All these issues could have resulted in the failure to measure

and record weights in older adults.

This lack of weight awareness was reported by a Japanese study where many elderly

individuals and their families were also often not aware of their weight41. Lack of

weight measurements among those with higher care needs was also reported, resulting

in them being unaware of any weight change. Although information on weight loss is

required in both the SGA and MNA, the unavailability of it does not affect the

completion of the MNA as it includes an option of “does not know”. Although the

SGA does not have the same option as the MNA, the overall rating of SGA does not


266

hinge on the quantity of weight loss alone; hence it could still be completed. But for

the SNAQ©, the lack of weight loss awareness limited its meaningful application.

Therefore it was modified to include the rating of the response ‘unsure’ or ‘yes,

unsure’.

Generally, this lack of awareness and records of weight history could potentially

result in misreporting of weight loss information. Therefore, an important implication

from this study finding is that the reliance of weight history as a key nutrition

indicator in many screening and assessment tools may not be entirely feasible, unless

exceptions for unknown responses are provided. This is particularly relevant for the

current generation of Singaporean older adults who have limited education and

literacy.


267

8.4 Limitations

8.4.1 Lack of weight measure on admission and disch arge

The availability of weight change data during hospitalisation was also relatively low

(69%) as only 85% of participants were fit to be weighed on admission and 75% at

discharge. The lack of weight measures on admission and discharge was either due to

refusal of participants, participants being unfit, or weighing was missed prior to

discharge. The exclusion of participants with incomplete weight data has important

implications on the results. It reduced the sample size for some analyses (e.g. MNA

and BMI related data analyses), resulting in the underestimation of malnutrition

prevalence. It also reduced the sample size for multiple logistic regression causing

potential type two errors. The absence of weight change data could also have

underestimated the prevalence of nutritional decline during hospitalisation. Missing

weight records were also reported in an Australian study where only three out of 100

elderly patients had their weight routinely taken and recorded during hospital

admission132.

Despite this limitation and its impact on the study results, this finding has reflected

the actual clinical situation and the difficulties involved in measuring weight in an

acute care setting among hospitalised older adults. It has highlighted the importance

of selecting screening and assessment tools which are not too reliant on admission

weight or its related derivative such as BMI. Alternative assessment approaches

should be considered when these measures are unavailable.


268

8.4.2 Lack of assessment on intake adequacy during

hospitalisation

There are numerous factors that potentially limit intake during hospitalisation

including duration of “nothing by mouth” order, nausea, vomiting, and poor appetite.

It had been reported that 21% elderly patients received inadequate intake during

hospitalisation27. A deficient intake during hospitalisation could lead to decline in

nutritional status e.g. weight loss. Hence an assessment of the intake adequacy and the

related barriers would have been useful in the study.

However, the additional information was not collected in this study for several

reasons. Patients’ nutrition intake during admission is often difficult to determine

accurately as it is mainly dependent on information from patients’ intake charts which

could be subjected to large variations. Factors influencing intake during

hospitalisation are not always documented during usual care of patients. It was

beyond the scope and resources of this research (PhD) project to obtain this

information separately from usual care and records. Moreover, nutrition intervention

would need to be initiated when poor intake is observed, hence confounding the

overall aims of the study.

Nevertheless, this study’s results have added to the scant data regarding change in

nutritional status during hospitalisation. A follow-on study would be useful to

examine reasons and potential strategies to prevent nutritional decline during

admission.


269

8.4.3 No nutritional status data at 3- and 6-months follow-up

It would have been useful to reassess the nutritional status of participants during the

follow-up periods. Change in nutritional status would potentially impact upon clinical

outcomes, meaning participants with improved nutritional status at 3- or 6-months

post-discharge may present with better outcomes. This effect may confound the

relationship between the baseline nutritional status and outcomes. However, the

nutritional status of the participants was not reassessed at both 3- and 6-month follow-

ups as there is no standardised outpatient follow-up system in the hospital upon

discharge. Some patients may be followed-up in the community instead of in the

hospital. Therefore, to perform the nutrition re-assessments would require the

participants to come for follow-up visits in the hospital solely for the purpose of the

study, as nutritional assessment cannot be conducted via phone calls. If a follow-up

visit was required for reassessment of the nutritional status in the present study, it may

have resulted in poor attendance, poor compliance and a higher drop-out rate.

Moreover additional resources would be required to fund the follow-up visits and

participant travel expenses which would be beyond the resources of the research

(PhD) project. The present study approach is consistent with most of the longitudinal

studies reported where the nutritional status was also not reassessed during the follow-

up94, 113, 157.

It was also not known if participants who received nutritional intervention during

admission complied with the nutrition advice upon discharge and if that led to

improved nutritional status during the follow-up period. If there was indeed an

improvement in the participants’ nutritional status post-discharge at 3- and 6-months,

the clinical outcomes may be less adversely affected. It is then possible that the


270

association between malnutrition and clinical outcomes would have been

underestimated or the effect size diluted.

8.4.4 Self-reporting of follow-up data

To ensure a low attrition rate and good compliance from participants, all follow-up

data were collected via telephone interview based on self-reporting by the participants

or their caregivers. The main limitation of self-reported information could be

underestimation. However, it has been shown that self-reported functional status via

phone interview was highly correlated with that assessed from performance testing367.

If there was any under-reporting of functional status in the study, the impact of

malnutrition on 6-month MBI in the present study could have been stronger than what

was reported.

8.4.5 Use of appropriate cut-offs for anthropometri c measures

As discussed, one of the key issues in nutritional assessment is the lack of a consistent

and standardised criterion for determining nutritional status. Even when consistent

measurements e.g. BMI was adopted, varying cut-offs were used. Particularly in the

older adults, there are no agreed consensus on the reference standards to be applied272.

Likewise, no references exist for other anthropometric measures such as MAC and

CAMA, and very often the usual reference standards for non-elderly population are

applied. Similarly in the present study, the cut-offs adopted for BMI and CAMA were

neither specific for the elderly nor for the Singaporean or Chinese population. This

created important limitations in the data interpretation.


271

One good example is the application of BMI for which there are no agreed consensus

on the appropriate cut-offs in older adults. Singaporeans have higher percentage body

fat and cardiovascular risks at lower a BMI compared to Caucasians264. If the same

holds true for the lower BMI ranges, then the BMI cut-off of 18.5kg/m2 applied in the

present study would have been too high, suggesting that a lower BMI cut-off is more

appropriate. However if lean body mass was compared instead of body fat at the

lower BMI ranges, a lower BMI cut-off would suggest an even lower lean body mass

which could be indicative of more severe nutrition depletion and malnutrition, hence

underestimating the prevalence of malnutrition.

On the other hand, raising the BMI cut-off for underweight may be appropriate as was

shown and proposed by other Caucasian-based studies conducted in older adults267,

268. But this would then potentially overestimate the prevalence of malnutrition. It

remains unclear if the cut-offs for the lower BMI range for classification of

underweight and malnutrition among Asians or Chinese would be any different from

the Caucasian population as no studies using more direct body composition measures

have been conducted. Therefore the sensitivity and predictive power of BMI in

identifying patients with poor nutritional status could be affected in the present study

unless more appropriate cut-offs were available and applied. Hence it is important that

future research is designed to define the appropriate BMI cut-offs for undernutrition

by validating them against body composition in the elderly Chinese.


272

8.4.6 Reliability of the TTSH NST

The TTSH NST showed the best diagnostic performance compared to the other three

nutrition screening tools when the SGA was used as the reference standard but only

when it was administered by the dietetic technician in the present study. When the

same tool was administered by the nurses routinely as part of the hospital nutrition

screening policy, the sensitivity was substantially reduced from 84% to 39%.

Although the inter-rater reliability of the TTSH NST between the dietitians and nurses

was shown to be >90% when the tool was developed (Appendix: TTSH Medical

Board Paper on TTSH NST), results from the present study showed otherwise (kappa

0.28; agreement 68%). Since the nurses did not complete the other screening tools

evaluated in the present study, it cannot be concluded if this poor reliability was

specific to the TTSH NST or related to the competency of the nurse assessors.

Although the TTSH NST was selected as the most appropriate nutritional screening

tool for application among hospitalised older adults in this study setting, performance

was dependent on the assessor and inter-rater reliability was poor between the dietetic

technician and nurses. The routine screening carried out by the nurses using the TTSH

NST failed to identify the malnourished patients adequately (64% not identified). For

the TTSH NST to maintain optimal sensitivity and performance, the dietetic

technician needs to administer the tool. However based on the hospital nutrition

screening policy, nurses are responsible for completing the screening tool upon

hospital admission. Hence for nurses to continue their role in administering the

screening tool with improved sensitivity, they would first need to be adequately

trained to complete the TTSH NST accurately.


273

However, there is a possibility that the deeper underlying issues surrounding the poor

sensitivity of the TTSH NST administered by the nurses were not limited to the

nurses’ competency, but with their work culture, attitudes towards administering

nutrition screening, and available nursing resources amidst heavy nursing

responsibilities. Further training would prove futile and not sustainable if these are the

contributing factors.


274

8.5 Recommendations

8.5.1 Clinical practice implications

8.5.1.1 Nutritional screening and assessment

The present study has shown that one in three older adults admitted to acute care in

Singapore were assessed as malnourished and this was associated with adverse

clinical outcomes (LOS and 6-month mortality) even after adjustment for a

comprehensive range of covariates. From the validation of the screening and

assessment tools, the TTSH NST is the best screening tool that should be

administered by the dietetic technician amongst older adults upon hospital admission;

the SGA is identified as the reference standard for assessing malnutrition and is the

most appropriate nutrition assessment tool to use in hospitalised older adults.

Findings from the present study supported the use of the TTSH NST as the nutritional

screening tool, and the SGA as the nutritional assessment tool for hospitalised older

adults in Singapore. These results provided an evidence base for and confirm the

validity of the current nutritional screening and assessment tools used in the geriatric

patients from Tan Tock Seng Hospital. To extend the benefits of applying this

evidence-based dietetic practice nationally, it is recommended that all hospitals in

Singapore adopt policies to screen older adults on admission for malnutrition risk

using the TTSH NST within 24-48 hours of admission. For hospitals which have

existing screening tools, it is recommended that they validate their tools against the

SGA and compare with the TTSH NST to select the most appropriate tool for their

hospitals. Those who are identified to be at risk should undergo further nutritional


275

assessments by a dietitian using the SGA within 72 hours of admission so that any

appropriate nutrition interventions can be initiated.

The dietitian review during routine care highlighted a couple of issues. Firstly, the

nutrition screening policy plays an important role in the recognition of malnutrition

when it is administered accurately and properly. Since the routine screening

conducted by the nurses was inadequate to identify malnutrition risk on admission,

the TTSH NST should ideally be performed by a trained dietetic technician. However,

if it is not practical to have a dietetic technician to perform all the screening due to the

existing resources, nurses should be adequately trained to achieve similar diagnostic

performance on the TTSH NST. If repeated training of nurses still fails to improve the

sensitivity of the tool either due to high turnover or excessive work responsibilities of

nursing staff, this can be used as evidence to request for more dietetic technicians who

can better perform the role. Otherwise, selected nurses in the hospital could be

identified to specialise in the role of nutrition screening to ensure better performance

of the tool.

Secondly, dietitian referral may not be limited to the trigger from malnutrition risk on

admission. Patients who are well-nourished on admission or not at risk of

malnutrition, should not be neglected as they were shown to be as likely as those who

are malnourished to deteriorate nutritionally with weight loss >1% per week.

Moreover the majority (73%) of those who suffered weight loss in hospital were not

seen by a dietitian. Nutrition screening, assessment and monitoring should be an

ongoing effort during hospital admission since older adults are susceptible to

nutritional decline. Any decline or potential decline in nutritional status during


276

admission, regardless of admission nutrition status, may also trigger the dietitian

referral.

Future nutrition policies and protocols relating to close monitoring and identification

of patients who are at risk of nutritional decline could be made a shared responsibility

within the healthcare team. Criteria could be developed to recognise patients

vulnerable to nutritional decline during admission so that a nutritional care plan

and/or dietitian referral could be initiated to minimise the risk. For example, weekly

weight measurement could be incorporated into routine nursing care, and nutrition

intake could be monitored more closely and linked to a intervention plan i.e. patients

who consume <1/2 share of meals for >3 days should be reviewed by a dietitian or be

started on standard oral supplements.

Thirdly, there are no existing protocols for follow-up of nutritional care plan for

malnourished patients upon discharge at TTSH. Nutrition monitoring and evaluation

completes the whole nutrition care process15 and would be especially important for

patients with a relatively short hospital stay since their nutritional risk may persists

beyond admission. Therefore nutrition policies should incorporate the monitoring and

management plan for these patients after hospitalisation.

8.5.1.2 Routine weight measurements

The present study showed that almost three-quarters (73%) of participants were

unaware of their usual body weight. Although almost two-thirds (62%) claimed to

experience weight loss over the past 6 months, they were unable to quantify. Weight

measurements can be challenging to obtain among hospitalised older adults as shown


277

in this study, where only 85% had weight taken on admission as many were unfit to

be weighed during acute illnesses. Amongst those with weight measured, more than a

quarter (27%) experienced significant weight loss during hospitalisation in this study.

Without the information on weight history, there are limitations in performing a

complete and accurate nutritional assessment as weight loss is an important predictor

of malnutrition and risk factor in the elderly233, 234, 385. Although there are limitations

in using weight loss as a marker for decline in nutritional status during hospitalisation,

it is still a pertinent and useful variable to monitor112.

The availability of weight (85%) reported in this study was very much a best case

scenario as it was measured by the dietitian in the context of research. This is likely to

be much lower under usual care situation. Therefore, it is strongly recommended that

routine weighing of older adults be implemented across all care settings. It is

important that all older adults keep records of their weight at regular intervals. In the

present study, it was common for older adults to report that their weight had been

taken by doctors or nurses during their routine check-ups, but they either could not

recall or were not informed of the reading.

Healthy community-dwelling older adults should monitor their weight at 3-6 monthly

intervals. Those with chronic diseases should have their weight taken and recorded at

every visit to their doctor or at any healthcare facilities. It may be useful for older

adults to keep a health booklet which includes weight records similar to booklets

available for monitoring growth and development of newborn infants. This close

monitoring of weight status would allow any early signs of unintentional weight loss


278

to be detected and appropriate assessments and interventions to be implemented to

minimise the risk of developing malnutrition subsequently if the weight loss persists.

For older adults who are hospitalised, hospitals could implement policies to encourage

that all weights are taken and recorded on admission or as soon as the patient is fit for

weighing, and repeated weekly112. As older adults are frequently mobilised as part of

care, weighing could be done concurrently with link and potentially be linked with the

assessment with the physiotherapist or occupational therapist. Weighing should be

taken as seriously as any other routine parameters such as temperature and blood

pressure monitoring with accurate and well-calibrated weighing scales, including

chair and bed weighing equipment.

Besides close monitoring of weight during hospitalisation, accurate charting and

monitoring of patients’ intake112 are equally important as any decline in intake could

be identified early to prevent weight loss from occurring. Patients with weight loss,

poor intake, increased requirements, or any potential risks that could cause

deterioration in nutritional status should be identified for early intervention.

Indirectly, this approach allows for continual screening to occur during hospitalisation

and does not stop at the admission screen. It would identify patients who are at low

nutrition risk on admission, but subsequently progress to high nutrition risk during

admission.


279

8.5.1.3 Factors associated with malnutrition

Several factors associated with malnutrition upon admission have been identified in

the present study. Those who presented with swallowing impairment, poor appetite,

on modified texture diet, poor fitting dentures, poor functional status pre-morbid and

on admission (MBI), dementia, depression, more comorbidities (CCI), poor cognitive

function (AMT score) and low serum albumin levels, were more likely to be

malnourished. These results confirmed the importance of these clinical characteristics

in their associated with malnutrition. It is therefore important for all doctors, nurses

and dietitians in Singapore hospitals to practice closer monitoring of patients who

present with these risk factors on admission. Although it was unclear if these factors

were the causes or consequences of malnutrition from the present study, they are

nevertheless important in identifying patients who are at risk or are malnourished.

These characteristics should be considered in addition to the components of the

malnutrition screening tool i.e. underweight, diagnosis-related nutrition risk, weight

loss, and decrease intake.

8.5.1.4 Application of nutrition assessment measure s

The SGA is recommended as the reference standard for the assessment of nutritional

status in Singaporean hospitalised older adults. This recommendation is consistent

with the practice guidelines for malnutrition developed by the DAA, where the SGA

is one of the valid nutrition assessment tools that can be applied in acute care

adults112. However, the other nutritional measures need not be abandoned completely.

The MNA was almost as good as the SGA in its predictive ability for adverse clinical

outcomes. However its main limitation for application in the Singapore acute care


280

setting was the difficulty in obtaining BMI measurements, resulting in lower

completion rate for the MNA. Therefore, the MNA may be potentially useful in other

care settings including community, rehabilitation, and long-term care112, where

obtaining weight measurements are less limiting. The MNA may also be a useful

assessment for monitoring the nutritional status of older adults in the community. One

recommendation to improve the application of MNA in acute care setting is to modify

the scoring criteria when weight or BMI is not available so that more patients could

be included in the assessment. This modified MNA would then need to be revalidated.

CAMA has the potential to be used as an additional nutrition measure or alternative

measure to the SGA in hospitalised older adults to define malnutrition on admission.

Similar to the SGA, it had a high completion rate and was predictive of 6-month

mortality in the present study. In addition, CAMA is more sensitive than the SGA in

detecting a decline in nutritional status and muscle atrophy during hospitalisation

shown in the present study. It is also potentially useful as an alternative monitor for

nutritional status besides weight change, especially when weight cannot be measured.

8.5.2 Future practice and research

8.5.2.1 Nutrition screening and interventions in th e community

Although it remains important for appropriate nutrition screening and assessment to

be performed upon acute hospital admissions to identify the risk and presence of

malnutrition amongst hospitalised older adults, that alone is inadequate. Despite the

implementation of screening in hospitals and nutrition intervention, weight loss still

occurred before patients were discharged from the hospital back into the community,


281

as was shown in the present study and other previous studies59, 101, 137. This could

point to the importance of implementing nutritional screening, monitoring of

nutritional status i.e. weight changes, and nutrition interventions in the community,

which would be more timely272.

Two in every five older adults admitted to TTSH were at risk of malnutrition, and

malnutrition was shown to be associated with several important risk factors (also

discussed in section 8.5.1.3). These findings suggest that malnutrition prevention can

start in the community prior to admission. For example the Nutrition Screening

Initiative (NSI)386 which was established to heighten the awareness of healthcare

providers and older adults to the warning signs of poor nutritional health could be

used to guide the initiation of early nutrition interventions prior to hospitalisation387.

The NSI has been used to study the nutritional risk among 2605 community dwelling

older adults aged >55 years in Singapore56 and detected a malnutrition risk prevalence

of 30%. The most common contributions to nutritional risks identified were changing

food intake due to illness (40%), taking three or more different medications daily

(25%) and eating alone (15%). All these should be further studied to facilitate the

implementation of appropriate nutrition screening and interventions in the community

to potentially prevent and reduce malnutrition upon hospital admission and to

possibly reduced the risk of hospitalisation76.

8.5.2.2 Evaluating the effectiveness of nutrition i ntervention

Confirming the appropriate nutritional screening and assessment tools for

identification of malnutrition upon hospital admission only contributed to the initial

steps of the whole nutrition care process. Nutrition interventions should follow


282

nutrition assessments as they are critical steps in the prevention and management of

malnutrition107, 175. Therefore it is important that future research studies evaluate the

different types of nutrition interventions provided to these patients who are at risk or

who are malnourished, and whether these interventions have a positive impact on the

patients’ nutritional status leading to improved outcomes. These research studies

would potentially provide more evidence to support the importance of early nutrition

screening and assessment to identify patients who would benefit from early nutrition

intervention to reduce the risk of adverse outcomes associated with malnutrition.

8.5.2.3 Establishing population specific anthropome tric cut-offs

Anthropometric measurements used in the present study such as CAMA would be a

quick and useful nutrition measure if a single objective measure is required. Although

CAMA did not perform as well as an assessment tool compared to the SGA, it may be

better for monitoring nutritional status (will be discussed in 8.5.2.4). Future research

should be conducted to further evaluate the validity and usefulness of CAMA, as well

as the MNA and BMI in other care settings in Singapore for their potential

applications. At the same time, it would also be useful to establish the appropriate cut-

offs for some of the anthropometric measures (e.g. BMI and CAMA) which are

relevant to the Singapore or Asian population. With an appropriate cut-offs, the

usefulness and performance of these anthropometric measures in the older adults may

be different from what was shown in the present study.


283

8.5.2.4 Appropriate monitor for nutritional status during

hospitalisation

Some difficulties and limitations using anthropometric measures to monitor changes

in nutritional status during acute hospitalisation have been highlighted in the present

study. Weight loss data may not always be available, and even when available may

not necessarily reflect the changes associated with nutritional status. Other multi-

parameter tools, such as the SGA is probably more sensitive to nutritional changes

when monitored over a longer duration. CAMA demonstrates the potential to be used

as an alternative measure to weight for monitoring nutrition status during

hospitalisation with its ability to predict clinical outcomes shown in the present study.

However, it is not known if the derivative equations for CAMA are appropriate for

the study population and if that would affect the sensitivity of the measure.

Studies relating to monitoring nutritional status are limited and none have evaluated

the effectiveness measures to monitor change in status. Therefore more studies should

be designed to specifically address nutrition status monitoring. They should aim to

identify the measure that is most appropriate for application during hospitalisation,

and determine their sensitivity in detecting changes over a shorter duration. The

potential application of CAMA should be considered and further evaluated. With a

standard measure for detecting change in nutritional status, patients can be

appropriately monitored and comparisons between studies can be interpreted more

meaningfully.

Since an important limitation of this study is that nutritional status was not assessed

post-discharge, this will be an important inclusion in future studies. As the present


284

study has identified and recommended a standard nutritional screening (TTSH NST)

and assessment tool (SGA) for hospitalised older adults, this will enable the use of a

consistent nutrition assessment method in future studies for comparisons and

monitoring of nutritional status over time. Future studies should continue to contribute

towards evidence-based approaches for dietetic practice and evaluate the effectiveness

of nutrition intervention for older adults across the continuum of care.


285

8.6 Conclusion

In conclusion, this thesis clearly highlighted the high prevalence of admission

malnutrition in older adults in a Singapore acute care setting. The impact of

malnutrition on clinical outcomes such as mortality would inevitably increase overall

healthcare costs. This situation is likely to become worse as the Singapore population

ages, unless preventive strategies are implemented.

It is therefore pertinent that vulnerable older adults are systematically screened and

assessed so that timely and appropriate interventions can be provided upon hospital

admission. This study has validated tools for this purpose and therefore provides an

important evidence base to progress to future evaluation of nutrition intervention

studies which could include longer term post-discharge nutritional and clinical

outcomes.

With decline in nutritional status being prevalent during hospitalisation, it would be

potentially beneficial for nutrition monitoring to continue during admission and

beyond discharge. An appropriate measure for nutritional monitoring would have to

be further evaluated. Finally, to address malnutrition at the population level, nutrition

screening and intervention should be initiated in the community to minimise the

development of malnutrition prior to admission.


286

9 Appendices

List of Appendices Table A-1: Subjective Global Assessment (SGA) ........................................................288 Table A-2: Mini-Nutritional Assessment (MNA).........................................................289 Table A-3: Malnutrition Screening Tool (MST) ..........................................................290 Table A-4: Tan Tock Seng Hospital Nutritional Screening Tool (TTSH NST).............291 Table A-5: Nutritional Risk Screening 2002 (NRS 2002) ............................................292 Table A-6: Mini Nutritional Assessment – Short-Form (MNA-SF)..............................293 Table A-7: Short Nutritional Assessment Questionnaire (SNAQ©) ..............................294 Table A-8: Malnutrition Universal Screening Tool (MUST)........................................295 Table A-9: Multivariate logistic regression of SGA against LOS>11 days (n=224)......296 Table A-10: Multivariate logistic regression of MNA againts LOS>11days (n=224) ...296 Table A-11: Multivariate logistic regression of BMI against LOS>11days (n=224) .....297 Table A-12: Multivariate logistic regression of CAMA against LOS>11days (n=224).297 Table A-13: Multivariate logistic regression of SGA against discharge to higher level care (n=223)................................................................................................................298 Table A-14: Multivariate logistic regression of MNA against discharge to higher level care (n=223)................................................................................................................298 Table A-15: Multivariate logistic regression of BMI against discharge to higher level care (n=223)................................................................................................................299 Table A-16: Multivariate logistic regression of CAMA against discharge to higher level care (n=223) .......................................................................................................299 Table A-17: Multivariate logistic regression of SGA against readmission at 3-month (n=222) .......................................................................................................................300 Table A-18: Multivariate logistic regression of MNA against readmission at 3-month (n=222) .......................................................................................................................300 Table A-19: Multivariate logistic regression of BMI against readmission at 3-month (n=222) .......................................................................................................................301 Table A-20: Multivariate logistic regression of CAMA against readmission at 3-month (n=222) ............................................................................................................301 Table A-21: Multivariate logistic regression of SGA against mortality at 6-month (n=224) .......................................................................................................................302 Table A-22: Multivariate logistic regression of MNA mortality at 6-month (n=224)....302 Table A-23: Multivariate logistic regression of BMI against mortality at 6-month (n=224) .......................................................................................................................303 Table A-24: Multivariate logistic regression of CAMA against mortality at 6-month (n=224) .......................................................................................................................303 Table A-25: Multiple linear regression of SGA against 6- month MBI (n=234) ...........304 Table A-26: Multiple linear regression of MNA against 6-month MBI (n=234)...........304 Table A-27: Multiple linear regression of BMI against 6-month MBI (n=206) ............305 Table A-28: Multiple linear regression of CAMA against 6-month MBI (n=232) ........305 Table A-29: Multivariate logistic regression of SGA against LOS>11 days (n=267, sample from univariate analysis) .................................................................................306 Table A-30: Multivariate logistic regression of SGA against readmission at 3-month (n=260, sample from univariate analysis) ....................................................................306


287

Table A-31: Multivariate logistic regression of SGA against mortality at 6-month (n=266, sample from univariate analysis) ....................................................................307 Table A-32: Multivariate logistic regression of CAMA against mortality at 6-month (n=264, sample from univariate analysis) ....................................................................307 Table A-33: Predictive power of the four malnutrition definitions against LOS>/=11 days 308 Table A-34: Predictive power of the four malnutrition definitions against discharge to higher level care ..........................................................................................................309 Table A-35: Predictive power of the four malnutrition definitions against readmission at 3-month...................................................................................................................310 Table A-36: Predictive power of the four malnutrition definitions against mortality at 6-month.......................................................................................................................311 Table A-37: Medline search history.............................................................................312 TTSH Medical Board Paper on TTSH NST.................................................................313 Modified Severity of Illness Index, Wong et al 2004...................................................317 Patient Information Sheet ............................................................................................318 Consent Form..............................................................................................................320 Data Forms..................................................................................................................321 Presentation abstract 1: Lack of admission weight measure is predictive of mortality at 6-month...................................................................................................................325 Presentation abstract 2: Change in nutritional status of older adults during hospitalisation .............................................................................................................326 Presentation poster 2: Change in nutritional status of older adults during hospitalisation .............................................................................................................327 Presenation abstract 3: Prevalence, risk factors and outcomes of malnutrition in hospitalised older adults ..............................................................................................328 Presentation poster 3: Prevalence, risk factors and outcomes of malnutrition in hospitalised older adults ..............................................................................................329 Presentation Abstract 4: Subjective Global Assessment is clinically more useful than Mini Nutritional Assessment in hospitalised older adults.............................................329 Presentation Abstract 4: Subjective Global Assessment is clinically more useful than Mini Nutritional Assessment in hospitalised older adults.............................................330 Presentation poster 4: Subjective Global Assessment is clinically more useful than Mini Nutritional Assessment in hospitalised older adults.............................................331 Presentation abstract 5: Evaluating the validity of four nutritional screening tools in hospitalised older adults ..............................................................................................331 Presentation abstract 5: Evaluating the validity of four nutritional screening tools in hospitalised older adults ..............................................................................................332 Presentation poster 5: Evaluating the validity of four nutritional screening tools in hospitalised older adults ..............................................................................................333 Presentation abstract 6: Evaluating the validity of a nutritional screening tool in hospitalised older adults ..............................................................................................334 Presentation poster 6: Evaluating the validity of a nutritional screening tool in hospitalised older adults ..............................................................................................335 Presentation abstract 7: Nutritional status of geriatric patients in a Singapore acute hospital .......................................................................................................................336 Presentation poster 7: Nutritional status of geriatric patients in a Singapore acute hospital .......................................................................................................................337


288

Table A-1: Subjective Global Assessment (SGA)

A. History

1. Weight change

Overall loss in past 6 months

Amount = kg

Percentage loss = %

Change in past 2 weeks Increase / No change/ Decrease

2. Dietary intake change (relative to

normal)

No change

Change: Duration weeks

Suboptimal solid diet / Full liquid diet /

Hypocaloric liquids / Starvation

3. Gastrointestinal symptoms (that

persisted for >2 weeks)

None

Nausea / Vomiting / Diarrhoea

4. Functional capacity No dysfunction (e.g. full capacity)

Dysfunction: Duration weeks

Working subopitmally / Ambulatory /

Bedridden

5. Disease and its relation to nutritional

requirements

Primary diagnosis:

Metabolic demand:

No stress / Low / Moderate / High stress

B. Physical

Loss of subcutaneous fat (triceps, chest)

Muscle wasting (quadriceps, deltoids)

Ankle oedema

Sacral oedema

Ascites

Normal/ Mild/ Moderate/ Severe





C. SGA rating A= Well-nourished

B=moderately (or suspected of

being)malnourished

C= severely malnourished

(Detsky et al, 1987235)


289

Table A-2: Mini-Nutritional Assessment (MNA) Screening

A. Has food intake declined over the past 3 months due to loss of

appetite, digestive problems, chewing or swallowing difficulties

• Severe loss of appetite = 0

• Moderate loss of appetite = 1

• No loss of appetite = 2

B. Weight loss during the last 3 months

Weight loss greater than 3kg = 0

• Does not know = 1

• Weight loss between 1 and 3 kg = 2

• No weight loss = 3

C. Mobility

• Bed or chairbound = 0

• Able to get out of bed/chair but does not go out = 1

• Goes out = 2

D. Has suffered psychological stress or acute disease

• Yes = 0

• No = 1

F. Body mass index (BMI)

E. Neuropsychological problems

• Severe dementia or depression = 0

• Mild dementia = 1

• No psychological problem = 2

• <19 = 0

• 19-<21 = 1

• 21 -<23 = 2

• >23 = 3

Screening Score: (subtotal max 14 points) Score <11 = possible malnutrition, continue assessment

Assessment

G. Lives independently (not in a nursing home or hospital)

• No = 0

• Yes = 1

H. Takes more than 3 prescription drugs per day

• No = 0

• Yes = 1

I. Pressure sores or skin ulcers

• No = 0

• Yes = 1

K. Selected consumption markers for protein intake (Yes/No)

J. How many full meals does the patient eat daily?

• 1 meal = 0

• 2 meals =1

• 3 meals = 2

1. At least one serving of dairy products (milk, chesse, yogurt) per

day?

2. Two or more servings of legumes or eggs per week?

3. Meat, fish or poultry every day ?

• If 0 or 1 yes = 0

• If 2 yes = 0.5

• If 3 yes = 1

N. Mode of feeding

• Unable to eat without assistance = 0

L. Consumes two or more servings of fruits or vegetables per day?

• No = 0

• Yes = 1

M. How much fluid (water, juice, coffee, tea, milk…) is consumed per day?

• Less than 3 cups = 0

• 3 to 5 cups = 0.5

• more than 5 cups = 1

O. Self view of nutritional status

• Views self as being malnourished = 0

• Self fed with some difficulty = 1

• Self-fed without any problem = 2

P. In comparison with other people of the same age, how does the

• Is uncertain of nutritional state = 1

• Views self as having no nutritional problem = 2

Q. Mid-arm circumference (MAC) in cm

patient consider his/her health status?

Not as good = 0

Does not know = 0.5

As good = 1

Better = 2

• MAC less than 21 = 0

• MAC 21 t o22 = 0.5

• MAC 22 or greater = 1

R. Calf circumference (CC) in cm

• CC less than 31 = 0

• CC 31 or greater = 1

Assessment score (max 16 points):

Total screening and assessment score (max 30 points):

17 to 23.5 points = at risk of malnutrition

Less than 17 points = malnourished

(Guigoz et al, 1994339)


290

Table A-3: Malnutrition Screening Tool (MST)

Have you lost weight recently without trying? No Unsure

0 2

If yes, how much weight (kilograms) have you lost? 1-5 6-10 11-15 >15 Unsure

1 2 3 4 2

Have you been eating poorly because of a decrease appetite? No Yes

0 1

Total Score of 2 or more = patient at risk of malnutrition (Ferguson et al, 1999184)


291

Table A-4: Tan Tock Seng Hospital Nutritional Screening Tool (TTSH NST)

Indicators Scoring Diagnosis nutritional risk level

Low 0 Moderate 1 High 2

Physical appearance

Normal 0 Moderately underweight 1 Severely underweight 2

Diet intake adequacy over past 5 days or more

Normal 0 Reduced moderately 1 Reduced severely 2 Not available --

Unintentional weight loss over past 6 months

No 0 Unsure 1 Yes, 0.5 – 3.0kg 2 Yes, >3.0-7.0kg 3 Yes, >7.0kg 4 Yes, Unsure 2

Total Score∗∗∗∗ ∗∗∗∗IF SCORE IS 4 OR MORE, REFER TO THE DIETITIAN.


292

Table A-5: Nutritional Risk Screening 2002 (NRS 2002)

Table A-5a: NRS 2002 - Initial screening 1

Is BMI <20.5?

Yes No

2 Has the patient lost weight within the last 3 months? 3 Has the patient had a reduced dietary intake in the last week? 4 Is the patient severely ill? (e.g. in intensive therapy) Yes: If the answer is “Yes” to any question, the screening in Table 1b is performed No: if the answer is “No” to all questions, the patient is re-screened at weekly intervals. If the patient e.g. is scheduled for a major operation, a preventive nutritional care plan is considered to avoid the associated risk status. Table A-5b: NRS 2002- Final screening

Impaired nutritional status Severity of disease (increase in requirements) Absent Score 0

Normal nutritional status Absent Score 0

Normal nutritional requirements

Mild Score 1

Wt loss >5% in 3 months or Food intake below 50-75% of normal requirement in preceding week

Mild Score 1

Hip fractures, chronic patients, in particular with acute complications:cirrhosis, COPD. Chronic hemodialysis, diabetes, oncology

Moderate Score 2

Wt loss >5% in 2 months or BMI 18.5-20.5 + impaired general conditions or Food intake 25-60% of normal requirements in preceding week

Moderate Score 2

Major abdominal surgery, stroke. Severe pneumonia, hematologic malignancy

Severe Score 3

Wt loss >5% in 1 month (>15% in 3 months) or BMI <18.5 + impaired general conditions or Food intake 0-25% of normal requirements in preceding week

Severe Score 3

Head injury, bone marrow transplantation, intensive care patients (APACHE>10)

Score: + Score: = Total score Age If >70 years: add 1 to total score above = age-adjusted total score Score >3: the patient is nutritionally ar risk and a nutritional care plan is initiated Score <3: weekly rescreening of the patient. If the patient e.g. is scheduled for a major operation, a preventive nutritional care plan is considered to avoid the associated risk status. (Kondrup et al, 2003166)


293

Table A-6: Mini Nutritional Assessment – Short-Form (MNA-SF)

Screening

A. Has food intake declined over the past 3 months due to loss of appetite, digestive

problems, chewing or swallowing difficulties

• Severe loss of appetite = 0

• Moderate loss of appetite = 1

• No loss of appetite = 2

B. Weight loss during the last 3 months

Weight loss greater than 3kg = 0

• Does not know = 1

• Weight loss between 1 and 3 kg = 2

• No weight loss = 3

C. Mobility

• Bed or chairbound = 0

• Able to get out of bed/chair but does not go out = 1

• Goes out = 2

D. Has suffered psychological stress or acute disease

• Yes = 0

• No = 1

E. Neuropsychological problems

• Severe dementia or depression = 0

• Mild dementia = 1

• No psychological problem = 2

F. Body mass index (BMI)

• <19 = 0

• 19-<21 = 1

• 21 -<23 = 2

• >23 = 3

Screening Score: (subtotal max 14 points)

Score <11 = possible malnutrition, continue assessment

(Rubenstein et al, 2001213)


294

Table A-7: Short Nutritional Assessment Questionnaire (SNAQ©)

Did you lose weight unintentionally More than 6 kg in the last 6 months More than 3 kg in the last month

3 2

Did you experience a decreased appetite over the last month? 1

Did you use supplemental drinks or tube feeding over the last month?

1

Total Score: Score >2 moderately malnourished Score > 3 severely malnourished

(Kruizenga et al, 2005179)


295

Table A-8: Malnutrition Universal Screening Tool (MUST)

Step 1: BMI score >20 kg/m2 18.5-20 kg/m2 <18.5 kg/m2

0 1 2

Step 2: Weight loss <5% 5-10% >10%

0 1 2

Step 3: Acute disease effect score If patient is acutely ill and there has been or is likely to be no nutritional intake for >5 days

2

Step 4: Overall risk of malnutrition Low Risk – Routine clinical care Medium Risk - Observe High Risk – Treat

Total score 0 1 >2

(Stratton et al, 2004198)


296

Table A-9: Multivariate logistic regression of SGA against LOS>11 days (n=224)

95.0% C.I.for EXP(B)

B Sig. Exp(B) Lower Upper

SGA – Malnourished vs Well-nourished* .895 .007 2.448 1.273 4.709

Age -.008 .747 .992 .945 1.041

Gender (Female vs Male) -.082 .794 .922 .499 1.702

Race (Chinese vs Non-Chinese) -.419 .301 .658 .297 1.455

Dementia (Yes vs No)* -1.528 .000 .217 .092 .511

Depression (Yes vs No)* -.836 .022 .433 .211 .888

Total number prescribed drugs -.098 .099 .907 .807 1.019

Severity of Illness Score .385 .390 1.470 .611 3.537

Charlson’s Co-morbidity index .105 .524 1.111 .805 1.533

Admission MBI* -.028 .000 .972 .959 .986

Constant 1.906 .340 6.724

SGA=Subjective Global Assessment; LOS= Length of Stay; MBI=Modified Barthel Index. R2=0.232; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-10: Multivariate logistic regression of MNA againts LOS>11days (n=224)



MNA – Malnourished vs Well-nourished .080 .844 1.084 .487 2.410

Age -.009 .701 .991 .944 1.039




Depression (Yes vs No) -.700 .054 .496 .243 1.013




Admission MBI* -.030 .000 .971 .957 .985

Constant 2.329 .237 10.271

MNA=Mini-Nutritional Assessment; LOS= Length of Stay; MBI=Modified Barthel Index. R2=0.195; Reference group is in bold. *Significant predictor of outcome, p<0.05


297

Table A-11: Multivariate logistic regression of BMI against LOS>11days (n=224)



BMI – Malnourished vs Well-nourished .723 .060 2.061 .971 4.376

Age -.011 .665 .989 .943 1.038








Admission MBI* -.028 .000 .973 .959 .987

Constant 2.040 .303 7.694

BMI=Body Mass Index; LOS= Length of Stay; MBI=Modified Barthel Index. R2=0.213; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-12: Multivariate logistic regression of CAMA against LOS>11days (n=224)



CAMA – Malnourished vs Well-nourished .336 .342 1.399 .700 2.796

Age -.010 .682 .990 .944 1.039








Admission MBI* -.029 .000 .972 .958 .986

Constant 2.345 .232 10.437

CAMA=corrected Arm Muscle Area; LOS= Length of Stay; MBI=Modified Barthel Index. R2=0.199; Reference group is in bold. *Significant predictor of outcome, p<0.05


298

Table A-13: Multivariate logistic regression of SGA against discharge to higher level care (n=223)



SGA – Malnourished vs Well-nourished -.202 .575 .817 .403 1.656

Age .000 .988 1.000 .953 1.050

Gender (Female vs Male)* -.942 .004 .390 .204 .744


Dementia (Yes vs No) -.778 .071 .459 .197 1.070



Severity of Illness Score -.830 .140 .436 .145 1.313

Charlson’s Co-morbidity index -.013 .937 .987 .713 1.365

Admission MBI* -.021 .005 .980 .966 .994

Constant 3.683 .104 39.764

SGA=Subjective Global Assessment; MBI=Modified Barthel Index. R2=0.232; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-14: Multivariate logistic regression of MNA against discharge to higher level care (n=223)



MNA – Malnourished vs Well-nourished -.476 .305 .621 .250 1.544

Age -.002 .923 .998 .950 1.048








Admission MBI* -.022 .003 .978 .964 .993

Constant 3.824 .094 45.770

MNA=Mini-Nutritional Assessment; MBI=Modified Barthel Index. R2=0.195; Reference group is in bold. *Significant predictor of outcome, p<0.05


299

Table A-15: Multivariate logistic regression of BMI against discharge to higher level care (n=223)




Age .000 .995 1.000 .952 1.050








Admission MBI* -.019 .010 .981 .967 .995

Constant 3.608 .113 36.882

BMI=Body Mass Index; MBI=Modified Barthel Index. R2=0.213; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-16: Multivariate logistic regression of CAMA against discharge to higher level care (n=223)




Age .001 .973 1.001 .954 1.051








Admission MBI* -.019 .009 .981 .967 .995

Constant 3.597 .114 36.473

CAMA=corrected Arm Muscle Area; MBI=Modified Barthel Index. R2=0.199; Reference group is in bold. *Significant predictor of outcome, p<0.05


300

Table A-17: Multivariate logistic regression of SGA against readmission at 3-month (n=222)



SGA – Malnourished vs Well-nourished .399 .225 1.491 .782 2.842

Age .016 .493 1.017 .970 1.066


Race (Chinese vs Non-Chinese) .812 .071 2.253 .932 5.443

Dementia (Yes vs No) .226 .543 1.254 .605 2.601

Depression (Yes vs No) .579 .082 1.784 .929 3.426

Total number prescribed drugs .099 .075 1.104 .990 1.231



Admission MBI -.008 .224 .992 .979 1.005

Constant -3.518 .076 .030


Table A-18: Multivariate logistic regression of MNA against readmission at 3-month (n=222)




Age .019 .428 1.019 .972 1.069








Admission MBI -.007 .285 .993 .980 1.006

Constant -3.606 .070 .027



301

Table A-19: Multivariate logistic regression of BMI against readmission at 3-month (n=222)



BMI – Malnourished vs Well-nourished -.321 .411 .725 .338 1.558

Age .016 .497 1.016 .970 1.065




Depression (Yes vs No)* .677 .042 1.969 1.025 3.782




Admission MBI -.011 .089 .989 .976 1.002

Constant -3.178 .108 .042


Table A-20: Multivariate logistic regression of CAMA against readmission at 3-month (n=222)




Age .016 .513 1.016 .969 1.065








Admission MBI -.010 .150 .991 .978 1.003

Constant -3.328 .092 .036



302

Table A-21: Multivariate logistic regression of SGA against mortality at 6-month (n=224)



SGA – Malnourished vs Well-nourished .860 .121 2.363 .797 7.006

Age .016 .669 1.017 .943 1.096







Charlson’s Co-morbidity index* .525 .024 1.690 1.072 2.664

Admission MBI -.005 .672 .995 .974 1.017

Constant -5.983 .096 .003


Table A-22: Multivariate logistic regression of MNA mortality at 6-month (n=224)




Age .021 .590 1.021 .947 1.101








Admission MBI -.002 .855 .998 .975 1.021

Constant -6.175 .086 .002



303

Table A-23: Multivariate logistic regression of BMI against mortality at 6-month (n=224)




Age .010 .798 1.010 .936 1.089








Admission MBI -.005 .667 .995 .974 1.017

Constant -5.600 .113 .004


Table A-24: Multivariate logistic regression of CAMA against mortality at 6-month (n=224)




Age .015 .688 1.016 .942 1.095








Admission MBI* -.007 .532 .993 .972 1.015

Constant -5.426 .121 .004



304

Table A-25: Multiple linear regression of SGA against 6- month MBI (n=234)

95.0% C.I.for EXP(B) Variables included in model B Sig. VIF Lower Upper

Admission MBI .738 .000 1.155 .625 .851

Dementia (Yes vs No) -13.490 .000 1.155 -20.142 -6.837

Constant 29.641 .000 - 21.171 38.112

Variables excluded: Age, gender, depression, total number of prescribed drugs, severity of illness score, Charlson’s co-morbidity index, SGA SGA=Subjective Global Assessment; MBI=Modified Barthel Index. R2=0.523; Reference group is in bold.

Table A-26: Multiple linear regression of MNA against 6-month MBI (n=234)


Admission MBI .664 .000 1.299 .548 .781

MNA (Malnourished vs Well-nourished) -14.023 .000 1.216 -21.374 -6.673


Constant 36.798 .000 - 27.745 45.851

Variables excluded: Age, gender, depression, total number of prescribed drugs, severity of illness score, Charlson’s co-morbidity index MNA=Mini-Nutritional Assessment; MBI=Modified Barthel Index. R2=0.545; Reference group is in bold.


305

Table A-27: Multiple linear regression of BMI against 6-month MBI (n=206)


Admission MBI .763 .000 1.199 .628 .898


Constant 26.849 .000 - 16.426 37.272

Variables excluded: Age, gender, depression, total number of prescribed drugs, severity of illness score, Charlson’s co-morbidity index, BMI BMI=Body Mass Index; MBI=Modified Barthel Index. R2=0.475; Reference group is in bold.

Table A-28: Multiple linear regression of CAMA against 6-month MBI (n=232)


Admission MBI .739 .000 1.162 .624 .853


Constant 29.632 .000 - 21.026 38.238

Variables excluded: Age, gender, depression, total number of prescribed drugs, severity of illness score, Charlson’s co-morbidity index, CAMA CAMA=corrected Arm Muscle Area; MBI=Modified Barthel Index. R2=0.522; Reference group is in bold.


306

Table A-29: Multivariate logistic regression of SGA against LOS>11 days (n=267, sample from univariate analysis)

95.0% C.I.for EXP(B) B Sig. Exp(B) Lower Upper

SGA – Malnourished vs Well-nourished* .751 .013 2.118 1.170 3.834

Age -.012 .613 .989 .945 1.034

Gender (Female vs Male) .176 .540 1.193 .679 2.096







Admission MBI* -.027 .000 .974 .963 .985

Constant 1.567 .391 4.793 SGA=Subjective Global Assessment; LOS= Length of Stay; MBI=Modified Barthel Index. R2=0.232; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-30: Multivariate logistic regression of SGA against readmission at 3-month (n=260, sample from univariate analysis)


SGA – Malnourished vs Well-nourished .640 .034 1.896 1.050 3.423

Age .016 .505 1.016 .970 1.063




Depression (Yes vs No) .791 .012 2.206 1.190 4.088



Charlson’s Co-morbidity index .114 .028 1.121 1.013 1.241

Admission MBI -.009 .108 .991 .980 1.002

Constant -2.863 .127 .057 SGA=Subjective Global Assessment; MBI=Modified Barthel Index. R2=0.232; Reference group is in bold. *Significant predictor of outcome, p<0.05


307

Table A-31: Multivariate logistic regression of SGA against mortality at 6-month (n=266, sample from univariate analysis)



SGA – Malnourished vs Well-nourished 1.111 .011 3.036 1.284 7.182

Age .026 .421 1.026 .963 1.094






Severity of Illness Score .409 .041 1.505 1.018 2.226

Charlson’s Co-morbidity index* -.083 .294 .920 .788 1.075

Admission MBI -.011 .173 .989 .974 1.005

Constant -5.981 .040 .003 SGA=Subjective Global Assessment; MBI=Modified Barthel Index. R2=0.232; Reference group is in bold. *Significant predictor of outcome, p<0.05

Table A-32: Multivariate logistic regression of CAMA against mortality at 6-month (n=264, sample from univariate analysis)


CAMA – Malnourished vs Well-nourished -0.907 .038 2.477 1.050 5.844

Age .024 .459 1.024 .961 1.092






Severity of Illness Score .006 .033 1.525 1.036 2.247

Charlson’s Co-morbidity index* -.410 .395 .936 .805 1.089

Admission MBI* .523 .130 .988 .973 1.003

Constant -5.426 .061 .005 CAMA=corrected Arm Muscle Area; MBI=Modified Barthel Index. R2=0.199; Reference group is in bold. *Significant predictor of outcome, p<0.05


308

Table A-33: Predictive power of the four malnutrition definitions against LOS>/=11 days

Sensitivity Specificity PPV NPV AUC SGA Model 0.748* 0.1 2 91 14 57 0.2 8 74 17 54 0.3 14 55 18 48 0.4 26 33 21 39 0.5 51 17 30 33 0.6 65 10 34 29 0.7 83 5 38 27 0.8 91 0 39 0 0.9 97 0 40 0 SGA only 41 76 55 66 MNA Model 0.718 0.1 0 96 0 58 0.2 4 83 15 55 0.3 13 56 17 48 0.4 32 38 26 44 0.5 52 21 31 38 0.6 75 7 36 28 0.7 85 3 38 22 0.8 94 2 40 25 0.9 98 0 41 0 MNA only 25 78 44 60 BMI Model 0.73 0.1 0 93 0 57 0.2 8 77 18 54 0.3 13 55 17 47 0.4 27 36 23 41 0.5 50 21 31 37 0.6 70 8 35 28 0.7 89 4 39 33 0.8 91 2 39 20 0.9 96 0 40 0 BMI only 30 80 52 62 CAMA Model 0.723 0.1 0 96 0 58 0.2 4 82 14 55 0.3 13 57 17 48 0.4 27 40 24 44 0.5 50 19 30 35 0.6 78 8 37 36 0.7 88 3 39 27 0.8 92 2 40 22 0.9 99 0 41 0 CAMA only 31 77 48 62 Abbreviations: PPV= Positive Predictive Value; NPV=Negative Predictive Value; AUC=Area Under Curve; SGA=Subjective Global Assessment; MNA= Mini-Nutritional Assessment; BMI= Body Mass Index; CAMA=corrected Arm Muscle Area. *Model with the largest AUC


309

Table A-34: Predictive power of the four malnutrition definitions against discharge to higher level care

Sensitivity Specificity PPV NPV AUC SGA Model 0.719 0.1 3 85 7 70 0.2 18 53 13 63 0.3 43 28 18 57 0.4 61 16 21 52 0.5 82 4 24 39 0.6 97 1 27 50 0.7 91 0 27 0 0.8 100 0 27 0 0.9 100 0 27 0 SGA only 32 68 27 73 MNA Model 0.723* 0.1 5 84 10 70 0.2 15 52 10 62 0.3 38 28 16 54 0.4 56 17 20 50 0.5 79 7 24 48 0.6 95 1 27 25 0.7 97 0 27 0 0.8 100 0 27 0 0.9 100 0 27 0 MNA only 19 75 22 72 BMI Model 0.721 0.1 5 85 11 70 0.2 15 55 11 63 0.3 41 32 19 59 0.4 57 19 21 54 0.5 80 5 24 40 0.6 97 2 27 60 0.7 97 0 27 0 0.8 100 0 27 0 0.9 100 0 27 0 BMI only 27 77 30 74 CAMA Model 0.718 0.1 2 85 4 70 0.2 15 54 11 63 0.3 41 32 18 59 0.4 57 18 21 53 0.5 79 3 23 28 0.6 97 1 27 50 0.7 97 0 27 0 0.8 100 0 27 0 0.9 100 0 27 0 CAMA only 27 74 28 73 Abbreviations: PPV= Positive Predictive Value; NPV=Negative Predictive Value; AUC=Area Under Curve; SGA=Subjective Global Assessment; MNA= Mini-Nutritional Assessment; BMI= Body Mass Index; CAMA=corrected Arm Muscle Area. *Model with the largest AUC


310

Table A-35: Predictive power of the four malnutrition definitions against readmission at 3-month

Sensitivity Specificity PPV NPV AUC SGA Model 0.674* 0.1 0 98 0 65 0.2 8 80 17 62 0.3 24 51 20 56 0.4 63 25 31 57 0.5 76 12 31 49 0.6 90 4 33 43 0.7 97 0 34 0 0.8 100 0 34 0 0.9 100 0 34 0 SGA only 40 73 44 70 MNA Model 0.674* 0.1 0 99 0 66 0.2 5 81 13 62 0.3 22 48 18 54 0.4 59 23 29 52 0.5 79 11 32 50 0.6 88 3 32 36 0.7 98 0 34 0 0.8 100 0 34 0 0.9 100 0 34 0 MNA only 33 82 48 70 BMI Model 0.661 0.1 0 99 0 66 0.2 4 77 8 61 0.3 28 53 23 58 0.4 57 29 29 56 0.5 80 11 32 52 0.6 95 1 33 20 0.7 99 0 34 0 0.8 100 0 34 0 0.9 100 0 34 0 BMI only 25 77 36 66 CAMA Model 0.663 0.1 0 99 0 66 0.2 5 79 11 62 0.3 24 48 19 55 0.4 54 27 28 53 0.5 78 10 31 45 0.6 93 1 33 17 0.7 99 0 34 0 0.8 100 0 34 0 0.9 100 0 34 0 CAMA only 30 76 39 67 Abbreviations: PPV= Positive Predictive Value; NPV=Negative Predictive Value; AUC=Area Under Curve; SGA=Subjective Global Assessment; MNA= Mini-Nutritional Assessment; BMI= Body Mass Index; CAMA=corrected Arm Muscle Area. *Model with the largest AUC


311

Table A-36: Predictive power of the four malnutrition definitions against mortality at 6-month

Sensitivity Specificity PPV NPV AUC SGA Model 0.733* 0.1 39 21 4 80 0.2 67 5 6 65 0.3 83 2 7 63 0.4 94 1 8 50 0.5 94 0 8 0 0.6 100 0 8 0 0.7 100 0 8 0 0.8 100 0 8 0 0.9 100 0 8 0 SGA only 56 70 14 95 MNA Model 0.730 0.1 33 20 4 78 0.2 61 5 5 59 0.3 83 2 7 63 0.4 94 1 8 50 0.5 94 0 8 0 0.6 100 0 8 0 0.7 100 0 8 0 0.8 100 0 8 0 0.9 100 0 8 0 MNA only 44 79 15 94 BMI Model 0.718 0.1 44 21 5 81 0.2 61 5 5 59 0.3 78 2 7 50 0.4 94 0 8 0 0.5 100 0 8 0 0.6 100 0 8 0 0.7 100 0 8 0 0.8 100 0 8 0 0.9 100 0 8 0 BMI only 39 77 13 94 CAMA Model 0.683 0.1 44 26 5 84 0.2 61 3 5 46 0.3 83 1 7 25 0.4 83 0 7 0 0.5 94 0 8 0 0.6 100 0 8 0 0.7 100 0 8 0 0.8 100 0 8 0 0.9 100 0 8 0 CAMA only 44 75 13 94 Abbreviations: PPV= Positive Predictive Value; NPV=Negative Predictive Value; AUC=Area Under Curve; SGA=Subjective Global Assessment; MNA= Mini-Nutritional Assessment; BMI= Body Mass Index; CAMA=corrected Arm Muscle Area. *Model with the largest AUC


312

Table A-37: Medline search history

The following tables outlined the search terms used for the search of Medline and CINAHL database via EBSCOhost from inception till Mar 2010. Topic: Malnutrition and nutritional assessment of hospitalised older adults

# Search History Results

S1 (MH "Protein-Energy Malnutrition") or (MH "Malnutrition") 9957

S2 ( (MH "Protein-Energy Malnutrition") or (MH "Malnutrition") ) or nutritional status 38869

S3 (hospital*) and (S2) 8282

S4 (MH "Nutrition Assessment+") 17403

S5 S3 and S4 1066

S6

S3 and S4 Limiters - English Language; Human; Age Related: Aged: 65+ years; Languages: English

476

Topic: Malnutrition and nutritional assessment of older adults

# Search History Results


S2 ( (MH "Protein-Energy Malnutrition") or (MH "Malnutrition") ) or nutritional status 38869

S3 (MH "Nutrition Assessment+") 17403

S4 mini nutritional assessment or subjective global assessment Limiters - English Language; Human;

582

S5 S2 and S4 483

S6 S2 and S4 Narrow by SubjectAge0: - Aged: 65+ years

219

Topic: Malnutrition and nutrition screening in olde r adults

# Query Results


S2 nutrition* screen* or Nutrition* risk* 1647

S3 *risk assessment or *risk factors or *risk 1281093

S4 S1 and S3 1837

S5 S2 and S4 212

S6 S2 and S4 Limiters - English Language; Human; Age Related: Aged: 65+ years

131


313

TTSH Medical Board Paper on TTSH NST


314


315


316


317

Modified Severity of Illness Index, Wong et al 2004


318

Patient Information Sheet

1. Study Information Protocol Title: Malnutrition and Clinical Outcomes of Hospitalised Elderly (Phase 2) Principal Investigator & Contact Details: Lim Yen Peng, 11 Jalan Tan Tock Seng, Singapore 308433, Contact Number 6 357 8689 Study Sponsor:

NHG Small Innovation Grant 2. Purpose of the Research Study

You are invited to participate in a research study. It is important to us that you first take time to read through and understand this information sheet. Nevertheless, before you take part in this research study, the study will be explained to you and you will be given the chance to ask questions. After you are properly satisfied that you understand this study, and that you wish to take part in the study, you must sign this informed consent form. You will be given a copy of this consent form to take home with you.

You are invited because you are admitted to one of the elderly wards in Tan Tock Seng Hospital.

This study is carried out to assess the nutritional status of the elderly patients in hospital and to determine if nutritional status is associated with outcomes such as length of stay and hospital readmissions.

This study will recruit 300 subjects from 2 elderly wards in Tan Tock Seng Hospital over a period of 6 months. 3. What procedures will be followed in this study

If you take part in this study, you/your relative will be interviewed on questions pertaining to your nutrition and function status. There will be two interviews at different times. The first will take approximately 10-20 minutes. The second will take approximately 30-45 minutes. We will obtain relevant information from the case-records. We will also take some simple body measurements such as weight, height, knee height, arm measurements, and perform a brief physical examination. The measurements will take approximately 20-30 minutes. Weight and arm measurements will be repeated upon discharge. None of these are invasive to you/your relative.

Blood samples will be analysed as part of this study. This includes lymphocyte count and serum albumin. This will be taken during the standard inpatient care procedures and no extra volume of blood will be taken for the purpose of this study. Additional analysis of serum albumin from the drawn blood samples may be requested at no additional cost to you/your relative.

Your participation in the study will last throughout your hospital stay, regardless of the duration and outcome. You will then be followed up for 6 months upon discharge. At 3-month and 6-month post-discharge, we will contact you/your relative to conduct a brief phone interview which will take approximately 15-20 minutes regarding your functional and general condition.

There will be no interference with the routine nursing or medical management. The doctor-in-charge will continue their usual medical care without interruptions. There will not be any extra medications or therapy given to you/your relative as a result of this study. You/your relative will NOT incur additional cost by participating in this study.

4. Your Responsibilities in This Study

If you agree to participate in this study, you should follow the advice given to you by the study team. You should be prepared to undergo all the assessments that are outlined above. However, you are able


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to choose not to undergo any of the assessments that you do not wish to do. 5. Possible Risks and Side Effects There is NO risk at all for you/your relative in taking part in this study. 6. Possible Benefits from Participating in the Study

There is no assurance you will benefit from participation in this study. However, your

participation in this study may add to the medical knowledge about the nutritional status and

certain issues related to nutrition which would in turn benefit other patients in future.

7. Voluntary Participation

Your participation in this study is voluntary. You may stop participating in this study at any time. Your decision not to take part in this study or to stop your participation will not affect your medical care or any benefits to which you are entitled. If you decide to stop taking part in this study, you should tell the Principal Investigator.

8. Confidentiality of Study and Medical Records

Information collected for this study will be kept confidential. Your records, to the extent of the applicable laws and regulations, will not be made publicly available.

Data collected and entered into the Case Report Forms are the property of Tan Tock Seng Hospital. In the event of any publication regarding this study, your identity will remain confidential.

9. Who To Contact if You Have Questions

If you have questions about this research study, you may contact the Principal Investigator, Ms Lim Yen Peng at 6 357 8689.

If you want an independent opinion of your rights as a research subject you may contact the NHG Domain-Specific Research Board Secretariat (Attn: Sujatha Sridhar) at 6471-3266.


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Consent Form

Protocol Title: Malnutrition and Clinical Outcomes of Hospitalised Elderly (Phase 2)

Principal Investigator & Contact Details: Lim Yen Peng, 11 Jalan Tan Tock Seng, Singapore 308433, Contact Number 6 357 8689

I voluntarily consent to take part in this research study. I have fully discussed and understood the purpose and procedures of this study. This study has been explained to me in _________________________________(language)

on ______________ (date) by _______________________________(name of translator).

I have been given enough time to ask any questions that I have about the study, and all my questions have been answered to the best of my doctor’s ability. _________________________ ____________________ ______________ Name of Patient Signature Date _________________________ ____________________ ______________ Name of Next of kin/ Signature Date primary caregiver (if participants are demented and incapable of giving consent) _________________________ ____________________ ______________ Name of Witness Signature Date

Investigator Statement I, the undersigned, certify to the best of my knowledge that the patient signing this informed consent form had the study fully explained and clearly understands the nature, risks and benefits of her participation in the study. _________________________ ____________________ ________________ Name of Investigator Signature Date


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Data Forms


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Data Forms


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Data Forms


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Data Forms


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Presentation abstract 1: Lack of admission weight measure is predictive of mortality at 6-month

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Background: Weight is a common anthropometric measurement, yet it is often not measured in hospitalised older adults. This study aims to determine the clinical and nutritional characteristics associated with the lack of admission weight measurement, and its associated impact on clinical outcomes. Methodology: Newly admitted patients aged > 60 years and who were not critically or terminally ill, were recruited from an acute geriatric medicine unit. We measured admission nutritional status using Subjective Global Assessment and functional status using modified Barthel Index (MBI). Participants were dichotomized into those with and without admission weight measures. Availability of admission weight measure was analysed against clinical outcomes such as length of stay (LOS), discharge to higher level care, 3-month readmission, 6-month mortality, and 6-month MBI using regression analysis with adjustment for age, gender, race, comorbidities, severity of illness, and admission MBI. Results: We studied 281 participants with mean age 81.3 + 7.6 years; 44% male; 83% Chinese; median length of stay 9 days. Weight measure was unavailable in 45 (16%) participants. In the group without admission weight measure, malnutrition prevalence was significantly higher (51% vs 32%, p<0.05) and mean MBI on admission was significantly lower (31.5+27.7 vs 64.3+25.2, p<0.05). Lack of admission weight was predictive of mortality at 6-month (33% vs 8%, OR 5.0, 95% CI 1.9-13.4) after adjustment for covariates. Discussion & Conclusion: Hospitalised older adults without weight measurement on admission represent an at-risk group with poorer nutritional and functional status and with five times higher risk of mortality at 6-month.


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Presentation abstract 2: Change in nutritional status of older adults during hospitalisation

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Background: The study aimed to determine the nutritional status of older adults upon admission to and discharge from a tertiary hospital in Singapore, and the impact of hospitalisation weight loss on clinical outcomes. Methodology: Newly admitted patients aged > 60 years were recruited from an acute geriatric medicine unit. All patients were assessed using Subjective Global Assessment (SGA), weight, mid-arm circumference (MAC), and triceps skinfold thickness (TSF) on admission and discharge. Weight loss >1% per week of hospitalization was analysed against the outcomes before and after adjustment for age, gender, depression, dementia, functional status, comorbidity and illness severity using regression analysis. Results: The sample comprised 281 patients with mean age 81.3 + 7.6 years; 44% male; 83% Chinese; median length of stay 9 days. 35% were malnourished on admission. 4% experienced decline in SGA status upon discharge. MAC and TSF were significantly lower at discharge (mean difference MAC:0.17cm; TSF:0.28mm, p<0.05). 27% had weight loss >1% per week during hospitalization. Weight loss >1% per week was predictive of discharge to higher level care (adjusted OR 2.64, p<0.05). Discussion & Conclusion: The decline in nutritional status of hospitalised older adults is significant. Hospitalisation weight loss is predictive of discharge to higher level care.


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Presentation poster 2: Change in nutritional status of older adults during hospitalisation


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Presenation abstract 3: Prevalence, risk factors and outcomes of malnutrition in hospitalised older adults

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Aim: Despite the rapidly ageing population in Singapore, there is limited data on malnutrition prevalence in older adults. The study aimed to determine the prevalence of malnutrition and to identify the associated risk factors and outcomes in hospitalized older adults. Methods: Newly admitted non-terminal patients aged > 60 years were recruited from an acute geriatric medicine unit. Nutritional status was assessed using Subjective Global Assessment (SGA) upon admission. Risk factors such as swallowing impairment, poor appetite, body mass index (BMI), weight loss, serum albumin, dentition, polypharmacy, functional status, dementia, depression and other clinical diagnoses were assessed. Nutritional status (by SGA) was analysed against length of hospital stay (LOS), modified Barthel Index (MBI) at discharge and 6 months, and 6-month mortality, before and after adjustment for age, gender, depression, dementia, severity of illness and admission MBI using regression analysis. Results: The sample comprised 281 patients with the following characteristics: mean age 81.3 + 7.6 years; 44% male; 83% Chinese; 33% dementia. SGA identified 35% as malnourished. Swallowing impairment, loss of appetite, weight loss, serum albumin, BMI, functional status, depression, dementia, cancer and urinary tract infection were significantly associated with malnutrition (OR range: 1.0-10.7, adjusted for age and gender). Malnutrition was predictive of LOS (adjusted p<0.05) and 6-month mortality (adjusted OR: 3.3, p<0.05). Conclusion: The local prevalence of malnutrition in hospitalised older adults is significant. Malnutrition is predictive of longer LOS and 6-month mortality. Identifying associated risk factors will help to detect patients who require further nutrition assessment and intervention.


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Presentation poster 3: Prevalence, risk factors and outcomes of malnutrition in hospitalised older adults


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Presentation Abstract 4: Subjective Global Assessment is clinically more useful than Mini Nutritional Assessment in hospitalised older adults

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Background: Malnutrition is prevalent among hospitalized older adults. None of the nutritional assessment tools had been evaluated for use in Singapore. The study aimed to compare the use of Subjective Global Assessment (SGA) and Mini-Nutritional Assessment (MNA) on hospitalized older adults. Methodology: Newly admitted patients aged > 60 years, who were not critically or terminally ill, were recruited from the geriatric medicine unit in Tan Tock Seng Hospital. Nutritional status was assessed using SGA and MNA upon admission, and analysed against clinical outcomes using regression analysis, before and after adjustment of covariates such as age, gender, race, comorbidities, severity of illness, and admission modified Barthel Index (MBI). Results: The sample comprised 281 participants with mean age 81.3 + 7.6 years; 44% male; 83% Chinese; median length of stay (LOS) 9 days. SGA and MNA were completed in 100% and 84% of participants; 35% and 23% were identified as malnourished, respectively. SGA-determined malnutrition was associated with LOS >11days (OR 1.94), readmission at 3-month (OR 2.42), mortality at 6-month (OR 4.30), and MBI <50 at 6-month (OR 2.08, all p<0.05). MNA-determined malnutrition was associated with readmission at 3-month (OR 2.15), mortality at 6-month (OR 2.97), and MBI<50 at 6-month (OR 5.80, all p<0.05). After adjustment for covariates, only SGA-determined malnutrition remained predictive of LOS >11days (OR 2.45, p<0.05). Discussion & Conclusion: SGA has a higher completion rate and is better associated with clinical outcomes than MNA. Therefore SGA is a more useful nutritional assessment tool for hospitalized older adults in Singapore.


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Presentation poster 4: Subjective Global Assessment is clinically more useful than Mini Nutritional Assessment in hospitalised older adults


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Presentation abstract 5: Evaluating the validity of four nutritional screening tools in hospitalised older adults

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Rationale: Hospitalised older adults are at risk of malnutrition due to a range of physiological and psychological reasons. There are limited nutritional screening tools which are validated in this high risk population in Singapore. The study aimed to determine and compare the diagnostic validity of four nutritional screening tools on hospitalised older adults in Singapore. Methods: Newly admitted patients aged > 60 years, who were not critically or terminally ill, were recruited from 3 wards of a geriatric medicine unit in Tan Tock Seng Hospital (TTSH). Nutritional screening was performed by a single Diet Technician on admission using four screening tools: TTSH Nutrition Screening Tool (NST), Nutrition Risk Screening 2002 (NRS 2002), Short Nutrition Assessment Questionnaire (SNAQ), and Mini Nutritional Assessment Short Form (MNA-SF). Nutritional status was assessed using Subjective Global Assessment (SGA) as the reference standard by a single Dietitian. ROC analysis and diagnostic performance of the tools were compared with SGA to determine AUC, sensitivity, specificity, positive and negative predictive values. Results: The sample comprised 281 patients with the following characteristics: mean age 81.3 + 7.6 years; 44% male; 83% Chinese; 33% dementia; 35% malnourished. The TTSH NST, NRS, SNAQ and MNA-SF identified 42%, 37%, 6% and 81% as at risk of malnutrition, respectively. The sensitivity, specificity, positive and negative predictive values of the screening tools against SGA were: TTSH NST (84%, 79%, 68%, 90%, AUC=0.87); NRS 2002 (69%, 79%, 64%, 83%, AUC=0.78); SNAQ (17%, 100%, 100%, 69%, AUC=0.76); MNA-SF (100%, 27%, 39%, 100%, AUC=0.16). The optimal cut-off for the TTSH NST remained unchanged even for patients aged >85 years (AUC=0.85). Conclusion: The TTSH NST has the best diagnostic performance among the four screening tools. It is a valid tool with good diagnostic value to detect malnutrition in the hospitalised older adults who are at high risk of malnutrition.


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Presentation poster 5: Evaluating the validity of four nutritional screening tools in hospitalised older adults

Presentation Abstract: Evaluating the validity of a nutritional screening tool in hospitalised older adults

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Aim: The Tan Tock Seng Hospital Nutrition Screening Tool (TTSH NST) was locally developed from a younger population of hospitalised adults. It is unclear if the previously derived cutoff of 4 would apply to the elderly. The study aimed to determine the diagnostic utility and predictive validity of the TTSH NST in hospitalised older adults, a high risk group for malnutrition and its sequelae. Methods: We prospectively screened 281 newly admitted patients aged 61 to 102 years for nutritional risk using the TTSH NST. The Subjective Global Assessment (SGA) served as the reference standard for comparison of nutritional status. Length of hospital stay (LOS), modified Barthel Index (MBI) at discharge and six months, and 6-month mortality were analysed in relation to NST-ascertained nutritional risk before and after adjustment for age, gender, dementia, depression, severity of illness and admission MBI using regression analysis. Results: The prevalence of malnutrition was 35% based upon SGA. The optimal cutoff of the TTSH NST was 4, yielding sensitivity, specificity, positive and negative predictive values of 84%, 79%, 68% and 90% respectively (AUC=0.87). The optimal cut-off remained at 4 even for patients aged >85 years (AUC=0.85). Risk of malnutrition (as determined by TTSH NST) was predictive of 6-month mortality (adjusted OR: 2.2, p=0.05), LOS (p<0.05), and MBI at discharge (p<0.05) but not at six months. Conclusion: Our findings show that using a cutoff of 4, the TTSH NST is a valid screening tool with good diagnostic utility and predictive validity for detecting malnutrition in hospitalised older adults.


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Presentation abstract 6: Evaluating the validity of a nutritional screening tool in hospitalised older adults

YP LIM1, WS LIM2, TL TAN2, L DANIELS3 1Nutrition and Dietetics, Tan Tock Seng Hospital, Singapore, 2Geriatric Medicine, Tan Tock Seng Hospital, Singapore, 3Institute of Health and Biomedical Institution, Queensland University of Technology, Australia Aim: The Tan Tock Seng Hospital Nutrition Screening Tool (TTSH NST) was locally developed from a younger population of hospitalised adults. It is unclear if the previously derived cutoff of 4 would apply to the elderly. The study aimed to determine the diagnostic utility and predictive validity of the TTSH NST in hospitalised older adults, a high risk group for malnutrition and its sequelae. Methods: We prospectively screened 281 newly admitted patients aged 61 to 102 years for nutritional risk using the TTSH NST. The Subjective Global Assessment (SGA) served as the reference standard for comparison of nutritional status. Length of hospital stay (LOS), modified Barthel Index (MBI) at discharge and six months, and 6-month mortality were analysed in relation to NST-ascertained nutritional risk before and after adjustment for age, gender, dementia, depression, severity of illness and admission MBI using regression analysis. Results: The prevalence of malnutrition was 35% based upon SGA. The optimal cutoff of the TTSH NST was 4, yielding sensitivity, specificity, positive and negative predictive values of 84%, 79%, 68% and 90% respectively (AUC=0.87). The optimal cut-off remained at 4 even for patients aged >85 years (AUC=0.85). Risk of malnutrition (as determined by TTSH NST) was predictive of 6-month mortality (adjusted OR: 2.2, p=0.05), LOS (p<0.05), and MBI at discharge (p<0.05) but not at six months. Conclusion: Our findings show that using a cutoff of 4, the TTSH NST is a valid screening tool with good diagnostic utility and predictive validity for detecting malnutrition in hospitalised older adults.


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Presentation poster 6: Evaluating the validity of a nutritional screening tool in hospitalised older adults


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Presentation abstract 7: Nutritional status of geriatric patients in a Singapore acute hospital

Y.P. Lim* 1, W.S. Lim2, T.L. Tan2, L, Y.H. Chan3, L. Daniels4 1Department of Nutrition and Dietetics, and 2Department Geriatric Medicine, Tan Tock Seng Hospital Singapore. 3Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore. 4Institute of Health and Biomedical Innovation, School of Public Health, Queensland University of Technology Objectives Older adults are at risk of malnutrition for a range of physiological and psychosocial reasons. The study aims to describe the nutritional status and determine the prevalence of protein-energy malnutrition of hospitalized older adults in Singapore. Design A prospective cross-sectional study. Setting Three wards of a geriatric medicine unit in an acute care general hospital Participants Newly admitted patients aged > 60 years, who were not terminally ill, were eligible. There was a consent rate of 71% among 141 eligible patients over a 3-month period. Our final sample comprised of 100 participants with the following characteristics: mean age 81.5 + 6.2 years; 52% male; 99% community dwellers; and 33% dementia. Measurements All participants were assessed by YPL using Mini-nutritional Assessment (MNA) and Subjective Global Assessment (SGA). Weight, knee height, demispan, mid-arm circumference (MAC) and calf circumference (CC) were measured. Serum albumin (ALB) and total lymphocytes count (TLC) were also analysed. Results 38% and 33% (MNA<17) were malnourished as determined by SGA and MNA respectively (kappa: 0.72). 37% were underweight (BMI < 18.5 kg/m2), and 51% were determined by MNA to be at risk of malnutrition. 57% had ALB less than 35g/L and 62% had TLC less than 1500/mm3. The mean BMI, MAC, CC and ALB were significantly lower (BMI: 20.7 vs 16.8kg/m2; MAC: 25.9 vs 22.4cm; CC: 30.5 vs 26.4cm; ALB: 33.5 vs 29.8g/L, all p<0.01) in the malnourished group (defined by SGA) compared with the non-malnourished group. Conclusion The prevalence of malnutrition among hospitalized older adults in an acute hospital in Singapore in this study is comparable with that reported in other prevalence studies in developed countries. There appears to be a good agreement between SGA and MNA for malnutrition assessment. Participants defined as malnourished according to SGA showed lower anthropometric and biochemical nutritional indices than well-nourished participants.


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Presentation poster 7: Nutritional status of geriatric patients in a Singapore acute hospital


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MALNUTRITION AND CLINICAL OUTCOMES IN ELDERLY PATIENTS ... · Malnutrition and Clinical Outcomes in Elderly Patients from A Singapore Acute Hospital ii Abstract Older adults, especially