The Core Components of Cardiac Rehabilitation for Health Related Quality of Life in Coronary Heart Disease

Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

by

Troy Anthony Francis

A thesis submitted in conformity with the requirements for the degree of Master of Science

Graduate Department of Pharmaceutical Sciences University of Toronto

© Copyright by Troy A. Francis, 2016

ii

The Core Components of Cardiac Rehabilitation for Health

Related Quality of Life in Coronary Heart Disease Patients: A

Systematic Review and Meta-Analysis of Randomized Controlled

Trials

Troy Anthony Francis

Master of Science

Graduate Department of Pharmaceutical Sciences

University of Toronto

2016

Abstract

Background: Cardiac rehabilitation (CR) is a comprehensive program offered to patients with coronary

heart disease (CHD). The aim of this study was to assess the effectiveness of providing any core

component of CR on health related quality of life (HRQOL) in adult patients with CHD.

Methods: We performed a systematic review, meta-analysis and meta-regression of randomized

controlled trials examining the core components of CR. Identified sources were published between

database inception and July 16th, 2014. Outcomes included overall, physical, emotional and social

HRQOL. Outcomes were reported as standardized mean change (SMC) with 95% confidence intervals.

Results: Summary effect sizes were (SMC 0.14; 95% CI 0.03 to 0.25), (SMC 0.23; 95% CI 0.08 to 0.38),

(SMC 0.11; 95% CI -0.03 to 0.24) and (SMC 0.03; 95% CI -0.07 to 0.13) for overall, physical, emotional

and social HRQOL respectively.

Conclusion: Receiving any CR intervention was shown to improve overall and physical HRQOL.

iii

Acknowledgments

I would like to acknowledge several people for guiding and encouraging me during my Masters

work. Foremost, I would like to thank my supervisor, Dr. Murray Krahn, and my advisor, Dr.

Valeria Rac, for providing me with this opportunity, and continuous support and motivation.

They both frequently inspired me and allowed me to develop independent thinking and research

skills, which has greatly assisted me throughout this whole process and will continue to do so in

the future.

Besides my advisors, I would like to express my gratitude to the rest of my thesis committee:

Petros Pechlivanoglou for his time, patience and mentorship; Nicholas Mitsakakis for his

encouragement and knowledge; and David Alter for his valued criticism during each step of my

research.

I wish to also thank my mentor, Nader Kabboul, who continually strives to make me a better

person. Without his direction, support and dedication none of this would have been possible.

Additionally, I would like to recognize Joanna Bieleki and the Toronto Health Economics

Technology Assessment Collaborative for their backing and commitment to this study.

Last but not the least, I would be remiss to not express my appreciation to my family and friends,

who have supported me on this journey and who constantly inspire me to accomplish more.

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Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Appendices ......................................................................................................................... ix

Chapter 1 Introduction .....................................................................................................................1

1 Introduction .................................................................................................................................1

1.1 Cardiac Rehabilitation and Secondary Prevention Programs ..............................................3

1.1.1 Core Components of Cardiac Rehabilitation ...........................................................4

1.1.2 Cardiac Rehabilitation’s Proposed Mechanism of Action .......................................6

1.1.3 Complexity of Cardiac Rehabilitation .....................................................................6

1.1.4 Psychosocial Outcomes of Cardiac Rehabilitation ..................................................7

1.1.5 Physical Outcomes of Cardiac Rehabilitation .........................................................8

1.2 Patient Reported Outcomes and Cardiac Rehabilitation ......................................................8

1.2.1 Health Related Quality of Life Instruments ...........................................................10

1.2.2 Health Related Quality of Life and Cardiac Rehabilitation ...................................12

1.2.3 Long-Term Sustainability of HRQOL after Cardiac Rehabilitation ......................13

1.3 Summary ............................................................................................................................14

1.4 Aim ....................................................................................................................................15

1.5 Hypothesis..........................................................................................................................15

Chapter 2 Methods .........................................................................................................................16

2 Methods .....................................................................................................................................16

2.1 Eligibility Criteria ..............................................................................................................16

2.1.1 Study Design ..........................................................................................................16

v

2.1.2 Information Sources ...............................................................................................17

2.1.3 Search Strategy ......................................................................................................18

2.1.4 Study Selection and Screening Process .................................................................18

2.1.5 Data Collection and Extraction ..............................................................................19

2.1.6 Risk of Bias and Quality Assessment ....................................................................19

2.2 Conceptualization of HRQOL Domains ............................................................................19

2.2.1 Challenges in Pooling Heterogeneous HRQOL Data ............................................20

2.3 Statistical Analysis .............................................................................................................21

2.3.1 Methods for Pooling Heterogeneous Data .............................................................21

2.3.2 Investigating Sources of Heterogeneity .................................................................22

2.3.3 Outcome Measurements.........................................................................................23

2.3.4 Health Related Quality of Life ...............................................................................24

2.3.5 Meta Regression.....................................................................................................24

2.3.6 Demographics ........................................................................................................26

2.3.7 Data Synthesis ........................................................................................................26

3 Results .......................................................................................................................................28

3.1 Study Demographics ..........................................................................................................28

3.1.1 Risk of Bias Assessment ........................................................................................29

3.2 Health Related Quality of Life ...........................................................................................30

3.2.1 Overall Health Related Quality of Life ..................................................................30

3.2.2 Physical Health Related Quality of Life ................................................................31

3.2.3 Emotional Health Related Quality of Life .............................................................32

3.2.4 Social Health Related Quality of Life ....................................................................32

3.3 Meta-Regression ................................................................................................................33

3.3.1 Overall Health Related Quality of Life ..................................................................33

3.3.2 Physical Health Related Quality of Life ................................................................33

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3.3.3 Emotional Health Related Quality of Life .............................................................34

3.3.4 Social Health Related Quality of Life ....................................................................34

4 Discussion .................................................................................................................................34

4.1 Health Related Quality of Life ...........................................................................................35

4.2 Meta Regression.................................................................................................................36

4.3 Strengths of Study ..............................................................................................................37

4.4 Study Limitations ...............................................................................................................37

4.5 Implications for Practice ....................................................................................................39

4.6 Future Directions ...............................................................................................................40

4.7 Conclusion .........................................................................................................................41

Tables .............................................................................................................................................42

Figures............................................................................................................................................56

Appendices .....................................................................................................................................79

vii

List of Tables

Table 1: Baseline Demographics .................................................................................................. 42

Table 2: HRQOL Instruments Used by Investigators ................................................................... 43

Table 3: Overall Health Related Quality of Life (Meta-Analysis) ............................................... 48

Table 4: Physical Health Related Quality of Life (Meta-Analysis) .............................................. 49

Table 5: Emotional Health Related Quality of Life (Meta-Analysis) ........................................... 50

Table 6: Social Health Related Quality of Life (Meta-Analysis) ................................................. 51

Table 7: Overall Health Related Quality of Life Regression Output............................................ 52

Table 8: Physical Health Related Quality of Life Regression Output .......................................... 53

Table 9: Emotional Health Related Quality of Life Regression Output ....................................... 54

Table 10: Social Health Related Quality of Life Regression Output............................................ 55

viii

List of Figures

Figure 1: PRISMA Flow Diagram Preferred Reporting Items for Systematic Reviews and Meta-

Analyses (PRISMA) ..................................................................................................................... 56

Figure 2: Risk of Bias Graph: review authors’ judgments about each risk of bias item presented

as percentages across all included studies .................................................................................... 57

Figure 3: Risk of Bias Summary: review authors’ judgments about each risk of bias item for each

included study ............................................................................................................................... 58

Figure 4: Overall Health Related Quality of Life Forest Plot ....................................................... 61

Figure 5: Overall Health Related Quality of Life Funnel Plot ...................................................... 62

Figure 6: Physical Health Related Quality of Life Forest Plot ..................................................... 63

Figure 7: Physical Health Related Quality of Life Funnel Plot .................................................... 64

Figure 8: Emotional Health Related Quality of Life Forest Plot .................................................. 65

Figure 9: Emotional Health Related Quality of Life Funnel Plot ................................................. 66

Figure 10: Social Health Related Quality of Life Forest Plot ....................................................... 67

Figure 11: Social Health Related Quality of Life Funnel Plot ...................................................... 68

ix

List of Appendices

Appendix 1 Literature Search Strategies .................................................................................. 79

Appendix 2 Standardized Mean Change Formula .................................................................... 85

Appendix 3 List of Validated HRQOL Instruments for use in CHD patients .......................... 86

Appendix 4 Characteristics of Included Studies ....................................................................... 91

Appendix 5 List of Excluded Studies (Full-Text Review Subset Only) .................................. 93

Appendix 6 R Source Code .................................................................................................... 164

1

Chapter 1 Introduction

1 Introduction

Cardiovascular disease (CVD) is the leading cause of mortality and a major cause of disability

across the globe in both adult men and women. CVD refers to a myriad of diseases involving the heart,

blood vessels and poor blood flow due to the hardening and narrowing of vascular walls leading to a

diseased cardiovascular system (1, 2). CVD accounts for an estimated 17.5 million deaths per year

globally and is expected to exceed 23.6 million by 2030 (3, 4). In Canada CVD claims more than 66

000 lives per year, which equates to one death every 7 minutes (5). The burden of CVD does not just

affect low and middle income countries but is increasingly a global health issue. With the advent of

globalization diets have changed and more people are consuming refined processed foods and as a

consequence are adopting a fast food culture. Increases in risk factors such as hypertension, obesity,

dyslipidemia and a sedentary lifestyle all contribute to the increase in CVD morbidity seen around the

world (6).

The global cost of CVD, including direct and indirect costs, is estimated to be US$863 billion,

and is projected to rise to US$1044 billion by 2030 (1). Currently the annual direct and indirect costs

of CVD per year in Canada are estimated to be $20.9 billion. (7). By 2020, it is estimated that the total

costs will reach $28.3 billion per year (8).

CVD represents more than one condition. CVD can be broken down into coronary heart disease

(CHD), stroke and congestive heart failure (CHF). A stroke occurs due to cerebral ischemia which

occurs when blood stops flowing to any part of the brain. Most strokes are ischemic and are caused by

blockages or a clot in blood vessels due to a buildup of plaque which causes damage to brain cells

which cannot be repaired. Stroke has been estimated to have caused 5.7 million deaths a year in 2005

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and the number of global deaths is projected to be 6.5 million in 2015 and 7.8 million in 2030 (9). CHF

can manifest itself in a variety of different ways but ischemic heart disease is thought to be a major risk

factor for CHF (10). CHF is known to have two subcategories; left ventricular dysfunction and

preserved ejection fraction, with preserved ejection fraction CHF having a better prognosis (11). CHF

is estimated to have a prevalence of 23 million worldwide, with a lifetime risk of developing CHF of

one in five (10).

CHD is the most common form of heart disease and reflects the narrowing of the blood vessels

supplying the heart muscle due to artheroscelorosis and presents symptoms as angina, acute coronary

insufficiency, and myocardial infarction (MI)(3, 12). The World Health Organization estimated that

CHD accounted for 7.4 million deaths a year globally according to 2012 statistics (3). In Canada CHD

is one of the leading causes of death which claims more than 33 600 lives per year (13). CHD

morbidity continues to rise globally through an increased number of post MI patients living longer

with CHD symptoms due to improvements in cardiac care (2, 12). The economic burden of CHD when

accounting for direct and indirect costs worldwide was estimated to be $108.9 billion and is predicted

to reach $218.7 billion by 2030 (4). In the current economic state the costs of CHD in Canada are

estimated to be $11 billion when measuring the economic burden of illness and are forecasted to reach

$17 billion by 2020 (7).

While there is no direct cure for CHD there are many possible treatments directed toward

slowing disease progression and preventing secondary events. Treatments for CHD include

pharmaceutical interventions, surgical procedures, lifestyle modification through secondary prevention

services and cardiac rehabilitation (CR). For those with established CHD pharmaceutical treatments of

statins, beta-blockers, angiotensin-converting enzyme inhibitors and anti-platelet medicine are

commonly prescribed to reduce the risk of MI (3). Patients with severe CHD can receive surgical

operations such as coronary artery bypass graft surgery (CABG) or percutaneous transluminal

3

coronary angioplasty (PCI) to treat the artherosclerosis and improve blood flow but the progression of

CHD will not change without lifesyle modification and drug therapy (14).

1.1 Cardiac Rehabilitation and Secondary Prevention Programs

CR is a secondary prevention program that aims to prolong survival from acute disease

manifestations through an improvement in day to day functionality (2). Secondary prevention services

work to reduce cardiac mortality and morbidity, through pharmacological therapy, surgical

interventions, and risk factor modification (15). CR programs have been created and promoted as a

way to recovery following acute coronary events and is defined as “…the coordinated sum of

interventions required to ensure the best physical, psychological and social conditions so that patients

with chronic or post-acute cardiovascular disease may, by their own efforts, preserve or resume

optimal functioning in society and, through improved health behaviours, slow or reverse progression

of disease” (16, 17). The use of CR can decrease the burden of CVD through a reduction in risk factors

and an improvement in functionality, while being found to be effective in patients with diagnosed

acute coronary syndrome, heart failure and those who have undergone coronary revascularization (18,

19).

The American Heart Association (AHA) and the American Association of

Cardiovascular and Pulmonary Rehabilitation (AACVPR) conclude that CR programs should

offer a multidisciplinary approach to CHD risk reduction and that programs should consist of

more than just exercise training alone (20, 21). While exercise training is an important factor in

CR, it is one element of many different therapies. The AHA and the AACVPR recommend that

all CR and secondary prevention services should contain specific core components that utilize

baseline patient assessment, nutritional counselling, risk factor management (lipids, blood

4

pressure, weight, diabetes mellitus, and smoking cessation), psychosocial interventions, physical

activity counseling and individualized exercise training (20, 21).

1.1.1 Core Components of Cardiac Rehabilitation

The core components of CR are an essential part of the contemporary care for patients

with CHD (22). Defining the core components of CR provides a foundation for programs

around the world to be build on, which can then be tailored for specific settings and populations.

The first core component is baseline patient assessment. This includes a physical examination to

determine the extent of the patients comorbidities and assesses their perceived health status.

This information can then be used to create individualistic treatment plans for each patient based

on their medical history and direct the implementation of further core components of CR (21).

The next core component is nutritional counselling performed by a registered dietitian or trained

health professional. This involves educating and prescribing specific dietary modifications to

closely match the therapeutic lifestyle change diet. The therapeutic lifestyle change diet

recommends the reduction of saturated fats, trans fats, cholesterol and sodium while increasing

the intake of fruits, vegetables, whole grains and fish into the diet (23).

The following core component is risk factor management which targets the modifiable

CVD risks such as hypertension, dyslipidemia, obesity, and a sedentary lifestyle (6). Weight

management regimens create achievable goals to reduce body weight in patients with a BMI >

25kg/m2

and/or waist circumference > 40 inches in men and > 35 inches in women. Blood

pressure management involves drug therapy and lifestyle modification in hypertensive patient’s

( ≥ 140 mm Hg systolic or ≥ 90 mm Hg diastolic). Lipid manangement is aimed at discovering

and treating those patients with dyslipidemia by obtaining fasting cholesterol, lipoprotein and

triglyceride levels (21, 24). Management of dyslipidemia involves drug therapy and dietary

5

changes (increasing plant sterol, fibre, and omega-3 fatty acid intake) (21). Diabetes

management screens for the presence of diabetes in all participants and if present educates the

patient on treatment options stressing the importance of complaince to diet, drug therapy and

blood sugar monitoring. Smoking cessation is offered to patients who are current smokers and

past smokers who have quit in the preceding 12 months and are likely to relapse. Patients are

given one on one or family counselling by a trained health professional to assist the smoker in

quitting and preventing relapse. Patients are offered pharmaceutical support in the form of

nicotine replacement therapy, bupropion or varenicline (24).

The subsequent core component is psychosocial management which is delivered by

registered psychologists or trained healthcare workers. Psychosocial management is designed to

identify psychological distress due to CHD using standardized instruments (22). In cases of

suspected depression or anxiety individual or group counselling was offered to patient’s and

family. Psychological interventions that are available include stress management which uses

cognitive behavioural strategies to help patient’s reduce or manage stress, as well as relaxation

and self-instruction training (25, 26). Physical activity has long been known to have a positive

effect on improving ones overall health and well being (6). The ensuing core component is

physical activity counselling. This includes the assessment of current physical activity levels

through questionnaires and pedometers and addresses readiness to change barriers to physical

activity. This information is then used by counsellors who encourage patients to gain 30-60

minutes of moderate intensity physical activity atleast 5 days a week and warns patient’s of

spontaneous vigourous physical activity risks (27).

The final core component is indivdualized exercise training. This component of CR is

based on the original baseline patient assessment from the physical examination. The

6

individualized exercise regimen is modeled on a rough aerobic exercise prescription of 3-5 days

a week at 50 - 80% of the patients exercise capacity for 20 – 60 minutes a session using any

continous modality such as walking or cycling. For resistance exercise patients are advised to

workout 2-3 days a week performing 1 – 3 sets of 10 – 15 repetitions performing calistenics,

free weights, and band exercises (21, 27).

1.1.2 Cardiac Rehabilitation’s Proposed Mechanism of Action

The main mechanistic evidence for how CR would work to improve one’s health

revolves around the exercise component. For patients with CHD exercise training has direct

benefits on the heart and coronary vasculature. Aerobic exercise has been shown to improve

myocardial oxygen demand, endothelial function, and autonomic tone, while reducing

inflammatory markers and clotting factors (28). The hypothesis is that the other core

components work to help reduce mortality and improve day to day functioning through an

indirect improvement in risk factors (lipids, smoking and blood pressure)(6, 29).

1.1.3 Complexity of Cardiac Rehabilitation

CR is a comprehensive program and shares many characteristics of a complex

intervention as defined by the Medical Research Councils 2000 Guidelines for developing and

evaluating complex interventions. In order for an intervention to be “complex” it needs to have a

number of interacting components, requires a number and difficulty of behaviors by those

delivering or receiving the intervention, there has to be a variability of outcomes and a degree of

flexibility or tailoring of the intervention is permitted (30).

Each secondary prevention service used in CR is distinct but when used together creates

the whole of the intervention. The ‘whole’ intervention refers to CR as a single entity and relates

to its ability to influence important health behaviors associated with CVD greater than the

7

individual use of the core components (31). The concept of “complex” interventions is relatively

new and as such there is a debate on how these interventions should be described and evaluated

(31). It is important to note that complex interventions are formed from many different parts

which could be material, human, theoretical, social or procedural in nature but are synergistic

when brought together (30, 31). CR should be thought of as the sum of its parts and each

component should be individually researched and evaluated for their overall benefit.

1.1.4 Psychosocial Outcomes of Cardiac Rehabilitation

The psychosocial and behavioral changes associated with CR are complicated.

Psychosocial dysfunction which is characterized as depression, anxiety and or social isolation is

normally seen in patients receiving CR (22). In order to determine if there was an association

between psychosocial disorders and cardiovascular events a large randomized multicenter trial,

Enhanced Recovery in Coronary Heart Disease Patients (ENRICHD), was performed (32). The

ENRICHD trial was conducted using 2481 MI patients (1084 women, 1397 men) with

diagnosed major or minor depression and low social support. Patients were randomized to a

cognitive behavoural psychosocial intervention or usual medical care and treated with selective

serotonin reuptake inhibitors, when indicated. The objective of this landmark study was to

determine whether treating depression and increasing social support in patients who recently

suffered an MI would reduce the risk of recurrent non fatal infarctions and sudden death (33).

The ENRICHD intervention did not improve event-free survival in comparison to patients

receiving usual medical care. However, depression and social isolation improved in both groups

but psychosocial interventions were unable to modify CHD (22, 33). In practice, it is generally

accepted that both men and women with varying degrees of CHD benefit from CR in terms of

quality of life and well-being.

8

1.1.5 Physical Outcomes of Cardiac Rehabilitation

Exercise based CR has been shown to improve physical outcomes in most groups of

patients with established CHD through an improvement in cardiovascular function leading to

improved strength and fitness (14). Exercise based CR has also been shown to significantly

reduce all-cause (13-25%) and cardiovascular mortality (26-37%) based on several systematic

reviews and meta-analysis (6, 8, 29). While, recent studies West et al, 2012 and Anderson et al,

2016 have stated that there is no significant difference in patients referred to CR in terms of

mortality these studies were underpowered and/or using outdated study designs (34, 35). More

recent unpublished research using indirect treatment comparisons has reinforced CRs ability to

reduce all cause and cardiovascular mortality (36).

CHD patients receiving psychological and education based interventions alone with no

associated exercise program have shown little or no influence on mortality or hospitalization

(25, 37). However, contemporary CR has transitioned from exercise only interventions to more

comprehensive secondary prevention programs that utilize all of the core components and has

been shown to provide the same overall mortality reduction as exercise based CR (22).

1.2 Patient Reported Outcomes and Cardiac Rehabilitation

The importance of highlighting patient centered care in designing and implementing a

comprehensive CR program allows for greater attention to sudden changes in overall health.

Working to improve a patients health status is an aspects of CR which is extremely important. A

patients health status is composed of their burden of symptoms, functional limitations and health

related quality of life (HRQOL) (38). The burden of symptoms a patient has to deal with refers

to the type and frequency of symptoms that has manifested in relation to their disease or the

9

medical treatment, while a patients functional status includes their physical, mental and social

limitations (39).

HRQOL is a multidimensional concept that represents a patient’s perception of the

discrepancy between actual and desired functional status and the overall impact of disease on

their own well-being (39, 40). An individual’s HRQOL is affected by factors such as

impairments, functional stress, perceptions and social opportunities and influenced by disease,

injury and treatment (41). Each patient has a varying degree to which symptoms, functional

limitations and medical interventions influence their well-being causing HRQOL to only

accurately be quantified by patient self-report (39).

Health status as related to quality of life consists of four domains that are important

measures for cardiac survivorship and provide prognostic information which reflect the aims of

CR (40). These four domains reflect subjective assessments of physical, emotional, and social

functioning as well as global perceptions on health (42). These four domains are conceptually

different but there is an overlap between outcomes because it is rare for an illness or disease to

affect only one domain.

Patient-reported outcomes (PROs) are any reports coming directly from patients about

how they feel in relation to a health condition and its therapy, without interpretation of their

responses by a clinician, or aid (43). PROs are important because they provide the patients

perspective on treatment benefit and provide another opportunity to measure treatment benefit

beyond survival, disease and physiological markers. They can also be used to report on

treatment satisfaction, HRQOL and compliance to treatments (44). PROs are sometimes used as

primary outcomes in clinical trials, predominantly when no other substitute measure of direct

benefit such as survival or death is available. Although clinical trials are incorporating more

10

PROs they are currently underused, and are usually used as secondary add on measures (39).

The lack of PROs being used as primary outcomes in clinical trials may be due to a perception

that these outcomes are “soft” and may not be useful in clinical practice or interpretable (38).

However, in previous prospective studies a patients health status has been shown to be a strong

independent predictor for health outcomes such as mortality, cardiovascular events and

hospitalization (45).

1.2.1 Health Related Quality of Life Instruments

PROs such as HRQOL can be collected using instruments that are disease and condition

specific or generic in nature. There are two varieties of measures that are currently used to

collect HRQOL scores. The first collection of instruments are health profiles which measure

HRQOL using individual scores of dimensions or domains and psychometric profiles which use

one or multiple scales to measure patient chracteristics or attributes. The second type of

instrument which can be used to conseptualize HRQOL are preference/utilty based measures

which can estimate HRQOL scores using either direct or indirect methods (46). Direct measures

of utility can be achieved through asking respondents to trade health states for different risks of

death or remaining years of life. While, indirect utilities can be achieved through HRQOL

questionnaires using weights or tariffs. Each item on the instruments questionnaire measures an

aspect of HRQOL, but in order for an instrument to detect significant effects of a treatment it

must be valid, reliable, responsive and interpretable (47).

The reliability of a tool refers to its capacity to produce dependable results over time

rated by Cronbachs’s alpha statistics. Test-retest reliability is an aspect of an instrument which

is a critical factor in making it consistent. Test-retest reliabilty measures if the repeated

administration of a measure to patients at different time points yields similar results (48).

11

Validity of an instrument seeks to determine the extent to which the tool measures what it is

intented to measure. Validity is assessed using criterion, face and construct validity. Criterion

validity seeks to measure the accuracy of the instrument in comparision to a gold standard. An

instrument has face validity if it contains items that reflect the specific disease or condition

being examined. While an instrument has construct validity if it is consistant with the concepts

being measured and relates to other tools (46, 48). The responsiveness and sensitivity of an

instrument is measured by its ability to detect clinically important changes in health status even

when they are small (47).

There is an overabundance of different instruments which could be used to measure

HRQOL in CHD patients, but not all measurement scales are equal and useful. The MacNew

HRQOL instrument is a disease specific measure for use in post-MI patients with CHD and has

previously been shown to be valid, reliable and responsive in both interviewer and self

administered modes. (49). Disease specific health surveys measure symptom burden, functional

limitations and HRQOL related to a specific disease state. Condition specific measures describe

patient symptoms or experiences related to a specific condition or a particular intervention or

treatment (39). The 36-Item Short Form Health Survey (SF-36) is one of the most readily used

and recognizable generic surveys. Generic measures are designed for use with any population

sample and summarize overall functional well being but do not give any information about

symptoms and functional limitations related to a specific desease. Generic HRQOL instruments

allow for comparisons between impact of treatments across diseases or conditions (44).

Preference based measures can also be used to measure health outcomes as a supplement to both

health profiles. The Euroqol quality of life scale (EQ-5D) is an indirect standardized and

validated measure which can be converted into a utility score and is applicable to many

conditions and treatments (50). Utility refers to the desirability or preference that individuals

12

exhibit for a condition (51). Patient health utilties are measured for various possible health states

and range between 0-1 (0 death, 1 perfect health) and represents ones overall health state (52).

When assessing HRQOL in patients with CHD both a disease specific and generic measure

should be used allowing for a more comprehensive assessment of health status in patients

receiving CR.

1.2.2 Health Related Quality of Life and Cardiac Rehabilitation

Over the years CR has been said to improve HRQOL when adherence rates to the

program are high by decreasing disease specific symptoms and increasing functional capacity

(53). Some improvement in HRQOL are attributed to the natural recovery process after cardiac

events, but CR has been shown to assist patients in reaching HRQOL scores similar to the

population norm (53). However, for all of the positive results following CRs ability to reduce

morbidity and mortality there are no meta-analyses which report on HRQOL in CHD patients

because of the heterogeneity in outcome measures and inconsistency in the reporting of

findings. A low HRQOL measured at baseline prior to CR has been shown to be one of the

strongest independent predictors of an improved response to CR (41).

There has been a lack of consistency in the reporting of CRs effect on HRQOL domains.

While it is anecdotally thought that receiving any core component of CR would improve

HRQOL in patients with CHD, it has not been systematically proven. Prior research into

physical dimension outcomes has shown that CR may improve physical functioning and

physical well-being when compared to controls who were not receiving structured exercise

therapy (54, 55). When comparing hospital based CR to home CR interventions no significant

difference in physical HRQOL domains were observed (54, 56). The age of the patients

engaging in CR may have an effect on their sensitivity to CR interventions. Older patients are

13

usually underrepresented in CR programs and may have an improvement in physical HRQOL

(57, 58). Lie et al, 2009 demonstrated that even interventions with no exercise component given

to CABG patients could improve physical domain HRQOL at 6 month follow-up, but no

between group differences were observed in comparison to the control.

Exploration into previous emotional or psychological domains has not revealed

consistent outcomes. On average CR was shown to improve state anxiety scores in patients but

did not have a significant effect on depression scores (56, 58). The psychological effects of CR

are difficult to quantify, while there are increases in particularly the SF-36 mental component

score there are no between group differences (59, 60). It is possible that psychological domain

effects may be represented in the physical domain (40). In terms of the social domain relatively

few trials employed instruments which aimed to measure social constructs. Dalal et al, 2007,

Robinson et al, 2011 and Roncella et al, 2013 reported social subscales scores of the MacNew

instrument and found there was no significant difference between groups at follow-up, however

improvements were reported. The same phenomenon was seen in other studies using the SF-36

social functioning subscale (34, 59, 61). The lack of consistent findings in the psychological and

social HRQOL domains could possibly be due to the sensitivity of the instrument or a lack of

adherence to the intervention by patients. Generic measures have primarily been used in all

trials evaluating HRQOL in CHD patients, most notably the SF-36 because of its ability to be

administered quickly. Generic measures may lack the sensitivity to change with cardiac

treatment in comparison to disease specific measures (29).

1.2.3 Long-Term Sustainability of HRQOL after Cardiac Rehabilitation

Even though there is a vast amount of evidence showing the benefit of CR, research has

not yet considered the effectiveness of CR programs in terms of long term sustainability of

14

health status. There are very few randomized controlled trials that observe the effects of CR

with a follow-up greater than one year which could possibly show its effects long term. There

have been many reports of improvements in health status achieved in the first year following the

intervention that were reduced as time went on. This was demonstrated by Murchie et al, 2004

and Cupples et al, 1999 who found that at 4-5 years follow-up improvements in health status

and HRQOL were reduced and no longer statistically significant between groups (62, 63). The

mode of exercise has also been associated with longer term sustainability of HRQOL benefits

when concurrent aerobic-strength training is performed in relation to aerobic training only (64).

1.3 Summary

The use of CR is a key tool in decreasing the burden of CHD through a reduction in

disease specific symptoms and increasing functional capacity. Unfortunately, no systematic

review has examined the effects of the core components of CR on overall HRQOL and HRQOL

domains among adult patients with CHD. The clinical effectiveness of CR on long-term

outcomes such as HRQOL is an area which has not been regularly explored. An overview of

cochrane reviews examining CR in CHD patients found that comparing HRQOL findings in CR

studies is difficult because of the complexity of the intervention, heterogeneity in the HRQOL

instruments, patient populations, and a lack of studies reporting patient reported health status

(12, 29). The AHA advocates for the inclusion of patient reported health status as a clinical

measure with an emphasis on using validated CHD specific instruments (39). Improvements in

patients HRQOL following CR and secondary prevention programs has not been consistently

reported. Accordingy, research is needed to explore the core components of CR effect on

HRQOL domains.

15

1.4 Aim

The aims of the present study were to evaluate the effectiveness of providing any core

component of CR delivered in the context of CR on overall, physical, emotional, and social

HRQOL domains in adult patients with CHD. Additionally, this study aimed to explore the

potential effect of the study-level predictors of CR and secondary prevention programs on

HRQOL in patients with CHD using meta-regression.

1.5 Hypothesis

The current study was premised on the hypothesis that receiving any core component of

CR would show an improvement on HRQOL domains based on exercise based CR effect on

reducing CVD morbidity and improving functional capacity. However, literature in this area is

limited but it is expected that whilst some HRQOL domains will change based on patients

receiving some non-pharmacological secondary prevention services it is theorized that exercise

will drive most of the improvement in HRQOL domains.

16

Chapter 2 Methods

2 Methods

A systematic review and meta-analysis of randomized controlled trials (RCTs) and

cluster RCTs examining CR interventions for CHD patients was performed (65).

2.1 Eligibility Criteria

2.1.1 Study Design

This systematic review included RCTs and cluster RCTs which utilized a pretest-

posttest-control (PPC) design. In the PPC design participants were randomized to the treatment

or control groups and each participant was measured before (baseline) and after (follow-up) the

treatment has been administered (66). The use of repeated measurements in the PPC design

allows each individual to be used as their own control, which typically increases the power and

precision of statistical tests (67). The PPC design compares the pre-post change in the treatment

group to the amount of change in the control.

Inclusion criteria: RCTs evaluating any core component of CR delivered in the context

of CR which measured patients HRQOL at baseline and follow-up in both the active and control

arms were included. Instruments could be generic or disease specific but needed to be validated

for us in CVD patients, and encompass one or all of the relevant HRQOL domains. Additionally

studies were required to have a minimum of six months follow up in order to be included in the

analysis.

Exclusion criteria: Studies using non-validated instruments for CVD patients were not

included. Studies which do not report both intervention and control group arm HRQOL scores at

17

baseline and follow-up, as well as studies of participants who completed cardiac rehabilitation

programs prior to randomization were also excluded.

Intervention: Based on the AHA and AACVPR scientific statement for CR and

secondary prevention programs (21). The focus was on the core components of CR: nutritional

counselling, risk factor management, psychosocial interventions, patient education, physical

activity counseling and individualized exercise training (20, 21).

Participants: The population consisted of adult men and women, who have had a

myocardial infarction (MI), have undergone revascularization (coronary artery bypass graft

(CABG) or percutaneous coronary interventions (PCI)), and who have angina pectoris or

coronary artery disease defined by angiography was included. Patients with heart failure, heart

valve surgery, heart transplants or implanted with either cardiac resynchronization therapy or

implantable defibrillators were excluded.

Comparators: The comparator (usual care / standard of care) could include standard

medical care, such as drug therapy, but patients were not randomized to receive any of the core

components of CR.

Setting: Hospital, community and home based settings.

Language: Only English language publications were included in the review.

2.1.2 Information Sources

Eligible studies were identified through a systematic search of the following databases:

Cochrane Central Register of Controlled Trials (CENTRAL), Health Technology Assessment

(HTA), and Database of Abstracts of Reviews of Effects (DARE) in The Cochrane Library,

18

MEDLINE, EMBASE, CINAHL, SCI-EXPANDED, PsychINFO and Web of Science (WOS),

all from their inception to July 16th

, 2014.

Reference lists of all identified systematic reviews and meta-analyses published since

inception of any of the above databases to July 16th

, 2014 were fully screened, and relevant titles

were imported for evaluation of their eligibility for this systematic review.

2.1.3 Search Strategy

The search strategy was designed with reference to those of the previous systematic

reviews evaluating the core components of CR (6, 8, 25, 29) and was conducted by an

information specialist experienced in systematic reviews (68). MEDLINE, EMBASE and

CINAHL were searched using a strategy combining selected MeSH terms and free text terms

relating to the core components of CR and coronary heart disease with RCT filters. The

MEDLINE search strategy was translated into the other databases using the appropriate

controlled vocabulary as applicable. Consideration was given to variations in terms used and

spellings of terms in different countries so that studies will not missed by the search strategy

because of such variations. The detailed search strategy used is this study is provided in

Appendix 1.

2.1.4 Study Selection and Screening Process

The titles and abstracts of all citations identified by the electronic searches were

examined for possible inclusion by two reviewers (NNK and TAF) working independently. Full

publications of potentially relevant studies were retrieved and reviewed by two reviewers (NNK

and TAF) who then independently determined study eligibility using a standardized inclusion

form. Any disagreements about study eligibility were resolved by discussion and, if necessary, a

third reviewer (MDK) was asked to arbitrate. Studies were excluded if they were a commentary,

19

editorial, or a letter to the editor. Masking was complete when outcome assessors were

concealed. Patient or performing physician masking was not deemed pertinent because of the

procedural nature of CR as an intervention.

2.1.5 Data Collection and Extraction

Data from included studies was extracted independently by two reviewers (NNK and

TAF) using a standardized data extraction tool. For each trial characteristics of the study, trial

population, intervention and outcome data were extracted and cross-checked. If data were

presented numerically (in tables or text) and graphically (in figures), the numeric data was used

because of possible measurement error when estimating from graphs. Any discrepancies were

resolved by the third reviewer (MDK).

2.1.6 Risk of Bias and Quality Assessment

In order to assess the quality of the included studies two reviewers (NNK and TAF)

independently assessed the risk of bias in included studies using the Cochrane Collaboration’s

recommended tool. The Cochrane Collaboration’s tool is a domain-based critical evaluation of

the following domains: sequence generation; allocation concealment; blinding of outcome

assessment; incomplete outcome data; and selective outcome reporting (69). Any disagreements

were resolved by a third reviewer (MDK). Assessments of risk of bias were provided in the risk

of bias table and summary for each study.

2.2 Conceptualization of HRQOL Domains

A subsequent literature review was performed to determine which HRQOL instruments

extracted from the retrieved studies were validated for use in CVD patients. Each instrument

was assessed for the specific patient population in which validation occurred as well as which

domains and subscales each instrument evaluated. The Quality of Life after Myocardial

20

Infarction (QLMI), MacNew Heart Disease Questionnaire (MacNew), Leiden Quality of Life

Questionnaire, Angina Pectoris Quality of Life Questionnaire (AP-QLQ), Seattle Angina

Questionnaire (SAQ), The Myocardial Infarction Dimensional Assessment Scale (MIDAS),

Quality of Life Index–Cardiac Version III (QLI), Short Form-36 (SF-36), Short Form -12 (SF-

12), Nottingham Health Profile (NHP), Dartmouth COOP Quality of Life instrument, Duke

Activity Status Index (DASI) , Cantril Ladder of Life, Short Form – 6D (SF-6D), Euroqol -5

Dimension (EQ-5D), and Time Trade Off (TTO) were deemed to be valid.

Based on CRs statement to improve a patient’s physical, psychological and social

conditions HRQOL outcomes were created to reflect these purported changes. HRQOL

outcomes were stratified into overall, physical, emotional and social HRQOL domains. Overall

HRQOL included perspectives on one’s life as a whole which also encompasses the physical,

emotional and social domains. Physical HRQOL included performance of self-care activities,

mobility and physical activities. Emotional HRQOL functions included mental health and

emotional reactions, while social HRQOL included social interactions, behaviours and isolation

(70).

2.2.1 Challenges in Pooling Heterogeneous HRQOL Data

CR is a complex intervention with many interacting components (31). The heterogeneity

in cardiac rehabilitation programs (patient population, and core components) and the multitude

of PRO instruments that can be used to measure HRQOL all create problems when attempting

to pool the data. When performing a meta-analysis of HRQOL outcomes challenges in

interpretation occur because of the different instruments used to measure similar constructs (71).

Additionally, because of various measures used interpreting the magnitude of the effect

becomes an issue from a clinical and decision making standpoint (72).

21

The underlying difficulty when attempting to understand HRQOL scores is providing a

meaningful interpretation of what those scores actually represent. While there is consensus that

HRQOL is an important endpoint in clinical trials there is still a glaring gap in how to use these

results in practice. In an attempt to make HRQOL results more meaningful researchers have

begun to look at the minimum important difference (MID) which is “the smallest difference in a

score in the domain of interest that patients perceive as important, either beneficial or harmful,

and that would lead to a change in the patients management”(47). While the concept of the MID

is a worthwhile attempt at helping to bring meaningfulness to interpreting HRQOL results, we

need to be mindful that these are estimates prone to sampling variation and influenced by the

patient population and should only be used as rough guidelines (73).

In order to pool HRQOL scores two important requirements must be met. First,

instruments scores must correlate with one another by measuring similar constructs in order to

be combined. Second, each measure must have similar responsiveness to change, even if small.

If instruments are less responsive than their counter parts treatment effects will be

underestimated and heterogeneity may be incorrectly attributed to differences in patients or

intervention (71, 72, 74).

2.3 Statistical Analysis

2.3.1 Methods for Pooling Heterogeneous Data

Researchers deal with the challenge of studies using multiple HRQOL measures to

measure the same construct by using an effect size summary statistic to standardize all scales to

a common metric (71). This research uses a repeated measure PPC design of HRQOL scores

and as such the metric of standardized mean change (SMC) was used. SMC is the mean pre-post

change in the treatment group minus the mean pre-post change in the control group, divided by

22

the pretest standard deviation (66). This approach will provide a single unit free estimate of

treatment effect in standard deviation units. The formulas used to calculate sampling variances

can be found in Appendix 2.

𝑔 = 𝑔𝑇 − 𝑔

𝐶= 𝐶(𝑛𝑇 − 1)

�̅�𝑝𝑜𝑠𝑡,𝑇 − �̅�𝑝𝑟𝑒,𝑇

𝑆𝐷𝑝𝑟𝑒,𝑇

− 𝐶(𝑛𝐶 − 1)�̅�𝑝𝑜𝑠𝑡,𝐶 − �̅�𝑝𝑟𝑒,𝐶

𝑆𝐷𝑝𝑟𝑒,𝐶

Where x̅ pre, T and x̅ post, T are the treatment group pretest and posttest means, SDpre, T is the

standard deviations of the pretest scores, C () is the bias-correction factor to account for the

overestimation of the effect sizes in small samples, nT is the size of the treatment group, x̅ pre C,

x̅ post C, SD pre C, and nC are the equivalent values for the control group and g represents sample

standardized mean change effect size (66).

A random effects multilevel meta-analysis was performed in order to account for some

of the heterogeneity that would be present when pooling these results to find the summary effect

of the intervention. The random-effects model estimates the mean of a distribution of effects.

Each study provides information about a different effect size, and the random effects model

incorporates each effect size into a summary estimate. In order to obtain the most precise

estimate of the summary effect in a random effects model both the within-study and between

study variances, tau2 (T

2), need to be known (75).

2.3.2 Investigating Sources of Heterogeneity

To investigate sources of variability in meta-analyses one of the most commonly utilised

methods to examine heterogeneity is meta-regression (76). Meta-regression merges meta-

analytic techniques with linear regression principles to determine whether a linear association

exists between explanatory variables and a comparative treatment effect (77-79). In meta-

regression, the outcome variable is the effect estimate (SMC) and the explanatory variables are

23

characteristics of the studies that might influence the size of intervention effect (80). A random

effects meta-regression was used in this study to allow for residual heterogeneity by assuming

the underlying effects follow a normal distribution (N) around the predictive covariates (76).

The effect size (gi) was estimated by the treatment effects (yi) in study i (i =,…,k). The

estimated variance (vi) of the treatment effects was assumed to be known. In the random effects

model, X represents the matrix of study level covariates and intercept, while β represents a

vector of the coefficients. The heterogeneity variance parameter, T2, represents the between

study variance (76).

Fixed effect meta-regression

yi ~ N(gi ,vi)

gi = Xiβ

Random effect meta-regression

gi ~ N(Xiβ,T2)

Meta-regression is a useful tool for investigating sources of heterogeneity in meta-

analysis when there are a large number of trials. However, meta-regression is an observational

meta-analytic technique and thus cannot be used to draw inferences about causal relationships

(76, 78).

2.3.3 Outcome Measurements

Study outcomes were measured at baseline (entry to CR) and at follow-up (minimum 6

months). The collected variables included overall, physical, emotional and social health related

quality of life, meta-regression proportion of variance explained and demographic information.

24

2.3.4 Health Related Quality of Life

The instruments used to measure overall HRQOL were the Cantril Ladder of Life,

MacNew, Leiden Quality of Life Questionnaire, AP-QLQ, QLI, Dartmouth COOP Quality of

Life instrument, QLMI, SF-6D, EQ-5D, and TTO. To determine physical HRQOL the MacNew,

Leiden Quality of Life Questionnaire, QLMI, DASI, AP-QLQ, SAQ, MIDAS, QLI, SF-36, SF-

12, and the NHP were used. Each of these subscales measured an aspect of physical functioning,

mobility or physical limitations. To evaluate emotional HRQOL the SF-36, SF-12, MacNew

Heart Disease Questionnaire, Leiden Quality of Life Questionnaire, QLMI, MIDAS, NHP, QLI,

and the AP-QLQ were used. These instruments all provided subscales on emotional limitations

or mental health. While, the social domain was created using social functioning subscales of the

MacNew, Leiden Quality of Life Questionnaire, QLMI, SF-36, NHP, and the QLI. A complete

breakdown of each validated instrument can be found in Appendix 3.

2.3.5 Meta Regression

The regression coefficient (β) obtained from a meta-regression analysis describes how

the treatment effect changes with a unit increase in the explanatory variables. In our analysis,

positive effect sizes indicate that the intervention had a better outcome than the control group.

The proportion of variance explained in the meta-regression analysis is calculated by comparing

the estimate of T2 with the covariate to T

2 when no covariate is used in the model (79).

𝑅2 = 1 − 𝑇2 𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑

𝑇2 𝑡𝑜𝑡𝑎𝑙

A univariable meta-regression was undertaken to explore potential treatment effect

modifiers. This study attempted to shed light on the complexity of CR through identifying

heterogeneity in the intervention, patient population and HRQOL instrument. Variables which

25

were shown to be important in the literature but were not previously assessed in a univariable

model were evaluated (12, 25, 29). Five a priori covariates were explored: year of publication,

duration of follow-up, the proportion of MI patients, type of CR intervention, and type of

instrument.

Year of publication was included as a continuous covariate in order to explore the

change in the standard of usual care over time in patients with established CHD to reflect the

improvement in pharmacological interventions. The duration of follow-up was explored as a

continuous potential treatment effect modifier in order to determine if the length of the follow-

up was associated with HRQOL scores. It was hypothesized that HRQOL scores would be

lower in studies that had longer follow-up because they assessed HRQOL farther from the

intervention. The proportion of MI patients was used as a continuous covariate in order to

explore if having more post-MI patients in the program was associated with HRQOL scores.

The type of CR intervention received (exercise vs non exercise) and (psychosocial management

only vs other core components) was explored to determine if type of intervention had a

differential effect on HRQOL scores. In order to evaluate type of intervention in the meta-

regression model two analyses were performed. Intervention was represented as a categorical

covariate with two levels, exercise and non-exercise. Exercise was used as the reference level in

the model. The second intervention analysis was represented as a categorical covariate with two

levels, psychosocial management only and other core components. Other core components were

used as the reference level in the model. The type of Instrument used was also included as a

categorical covariate in order to determine if there was a significant difference in the type of

measure used to conceptualize HRQOL scores. Three levels were used in the meta-regression

model generic, disease specific and preference based measures. Disease specific measures were

used as the reference level in each analysis. In cases were preference based measures were not

26

used meta-regression models only had two levels, generic and disease specific measures.

Disease specific instruments are said to be in general more responsive than generic instruments

which could possibly lead to an underestimation of treatment effect and as such had to be

investigated (81).

2.3.6 Demographics

Each study had demographic characteristics assessed at baseline extracted. Demographic

variables included: study location, publication date, age, gender, and comorbidities.

2.3.7 Data Synthesis

Data synthesis and analyses were performed using Microsoft Excel, and R software

using the package “metafor” (82, 83). A direct head-to-head pair-wise frequentist analysis was

used to compare receiving any core component of CR to usual care. Trials reported data on the

continuous outcome of HRQOL and/or HRQOL domains. Continuous outcomes are expressed

using the metric of standardized mean change (SMC) to combine data from different

instruments measuring the same constructs. A random-effects model was used to account for

residual heterogeneity. For all outcomes, data was collected from intention-to-treat (ITT)

analyses, but in cases where ITT results were not provided per protocol results were used. With

regards to the inconsistency in the reporting of outcomes in the absence of mean scores medians

were used as a replacement in order to include as many studies as possible. Additionally, in

cases where no standard deviations were given for associated means standard deviation were

estimated by transforming given standard errors or confidence intervals (Appendix 2).

A multilevel meta-analytic model using independent pairwise crossed random effects

were used in order to account for the correlation between HRQOL instruments and studies with

multiple reports of HRQOL outcomes.

27

𝑔𝑖𝑗 = 𝜷𝟎 + 𝜷𝟏𝑋1𝑖 + ⋯ + 𝜷𝒑𝑋𝑝𝑖 + 𝜂𝑖 + 𝜗𝑗 + 𝜖𝑖𝑗

Where gij is the observed effect sizes with i representing the study and j representing the

instrument, β0 is the intercept of the dependent variable; X represents the matrix of study level

covariates and intercept, while β represents a vector of the coefficients, Xki (k = 1, …p) p

covariates for study i, εij is the random error term accounting for the within study variance and

the variation between instruments, with ηi representing the study specific random effects and ϑj

representing the HRQOL instrument specific random effects (84).

Subgroup analysis by means of stratified meta-analysis using random and fixed effects

was performed according to the HRQOL instrument in order to observe individual subscale

scores. Random effects subgroup analysis was performed when there was a large amount of

studies; otherwise fixed effects models were used. Additionally, 95% confidence intervals (CI)

were calculated for each effect estimate. HRQOL meta-analysis results are represented using

forest plots. Post-test-pretest correlation coefficients estimates for each HRQOL instrument

needed for the SMC analysis were extracted from the literature or imputed based on expert

opinion. The correlation coefficients represent the relationship between an instrument’s baseline

and follow-up scores in relation to its reliability. In order to interpret the meta-analysis results

the criterion created by Cohen, 1988 which states that effect size changes of 0.2 SD units

reflects a small difference, 0.5 SD units a moderate difference and 0.8 SD units a large

difference were used (85).

Heterogeneity amongst included studies was quantitatively assessed using the I2 statistic,

tau2 (T

2) and qualitatively by comparing characteristics of included studies and visually

inspecting forest plots. Given that using the I2 statistic is not precise an uncertainty interval was

28

also given (86, 87). In order to interpret the inconsistency seen in I2 the guide of 0% to 40%:

minimal heterogeneity; 30% to 60%: moderate heterogeneity; 50% to 90%: substantial

heterogeneity; 75% to 100%: considerable heterogeneity (77).

A P-value of 0.10, rather than the conventional level of 0.05 were used to determine

statistical significance to account for domains with small sample sizes or low power. A

sensitivity analysis was undertaken to assess the various differences in imputed correlation

coefficients used in the SMC analysis. In order to examine small study and publication bias a

funnel plot and a rank correlation test were performed. A rank correlation test using Kendall’s

tau was performed to investigate for correlation between the effect size estimate and sampling

variance to identify possible publication bias (88).

3 Results

3.1 Study Demographics

Figure 1 outlines the selection of potentially eligible studies. A total of 1,270 potential

studies were identified, 1,205 were excluded because they were not randomized (n=142),

included the wrong patient population (n=138), did not evaluate an eligible intervention (n=55),

had a study duration of less than 6 months (n=66), did not report outcome of interest (n=750),

included patients who were randomized after CR (n=6), were not published in English (n=16),

did not report full outcome data (n=27) or were randomized before cardiac surgery (n=5). Sixty-

five reports of 52 RCTs with 13,360 participants were included in the multilevel meta-analysis.

The study and patient demographics are outlined in Table 1. Studies were conducted in

North America (25%), Australia (9.6%), Asia (7.7%) and Europe (58%). In terms of publication

dates studies ranged from 1990 – 1999 (8%), 2000 – 2009 (57%) and 2010 – 2014 (35%). The

29

mean age of participants was 62 years and 66% of the participants were male. Eleven percent of

patients were diagnosed with diabetes and 24% were previous smokers. In terms of studies

reporting the primary objectives 19 RCTs with 3,892 patients reported overall HRQOL, 46

RCTs with 12,523 patients reported physical HRQOL, 39 RCTs with 11,539 patients reported

emotional HRQOL, and 27 RCTs with 8,209 patients reported social HRQOL. A full list of

included and excluded studies can be found in Appendix 5 and 6 respectively.

3.1.1 Risk of Bias Assessment

All included trials were assessed using the Cochrane risk of bias assessment tool (69).

For each trial the risk of bias was presented using ‘risk of bias’ graph (Figure 2). In addition an

overall summary of risk of bias is given in Figure 3. Included RCTs ranged from the year 1995

to 2014.

Thirty-two trials (62%) were at low risk of selection bias due to the satisfactory

generation of the randomization sequence. One trial (2%) had a high risk of selection bias

because of a non-random method used to generate their sequence. Nineteen studies (37%) were

judged to have an unclear risk of selection bias because the method used to generate the random

sequence was not described in the paper. Thirty-one studies (60%) were at a low risk of

selection bias owing to proper concealment allocation of the intervention to participants and

investigators. One trial (2%) had a high risk of selection bias as the participants or investigators

could foresee assignment. Twenty studies (38%) were judged to have an unclear risk of

selection bias because the method of concealment was not described in detail allowing for a

definite judgement.

Twenty trials (38%) were at a low risk for performance bias because investigators and

key personal were blinded to allocation. Three studies (6%) were at a high risk of performance

30

bias owing to investigators and participants not being blinded to allocation. Twenty-seven

studies (52%) were judged to have unclear risk of performance bias. Seventeen trials (33%)

were at a low risk for detection bias because investigators were unaware of the allocation of

patients. Four studies (8%) were at a high risk of detection bias and thirty-one studies (60%)

were at a low risk of detection bias.

Thirty-eight studies (73%) were judged to be at low risk of attrition bias due to the

nature of handling of incomplete outcome data and four trials were measured to have an unclear

risk for attrition bias. Ten trials (19%) were at a high risk for attrition bias. Thirty-eight studies

(73%) were judged to be at low risk of reporting bias because based on information provided by

the authors regarding primary and secondary outcomes. Five studies (10%) were at a high risk

of selective reporting and nine studies (17%) were judged to have an unclear risk of selective

reporting.

3.2 Health Related Quality of Life

The QLMI, MacNew, Leiden Quality of Life Questionnaire, AP-QLQ, SAQ, MIDAS,

QLI, SF-36, SF-12, NHP, Dartmouth COOP Quality of Life instrument, DASI, Cantril Ladder

of Life, SF-6D, EQ-5D, and TTO were used to evaluate the changes in each HRQOL domain.

Table 2 outlines the specific studies, subscales and or domains which were used to

conceptualize each HRQOL domain.

3.2.1 Overall Health Related Quality of Life

Receiving any core component of CR improved overall HRQOL when compared to

usual care. Using 21 reports of 19 trials and 3,892 participants the SMC, with respect to overall

HRQOL was 0.14 (95% CI 0.03 to 0.25) (Figure 4). Using Cohen’s criteria, this would be

categorized as a small treatment effect. There was a substantial amount of clinical and statistical

31

heterogeneity when combining all the studies in the SMC model (I2

= 68%; 95% UI 44-87%).

From the I2

of 69% about 52% of the inconsistency was attributed to between study variance

with 16% of the inconsistency due to within study variance. Fixed effect subgroup analysis

results based on the MacNew, EQ-5D and TTO overall HRQOL scores were inconsistent in

showing an improvement in overall HRQOL. The SMC, with respect to the MacNew’s overall

HRQOL subscale was -0.04 (95% CI -0.12 to 0.04), while, the EQ-5D’s SMC was 0.04 (95%

CI -0.06 to 0.14). Based on Cohen’s classification of effect sizes no treatment effect was

observed. The SMC, with regard to the TTO was 0.31 (95% CI 0.12 to 0.49). In relation to

Cohen’s criteria this effect would be categorized as a small moderate effect. Table 3 provides an

outline of the overall summary effect and subgroup analysis of all the HRQOL instruments used

to measure overall HRQOL. The rank correlation test (tau = 0.3810) provided evidence of

publication bias (p = 0.02) in terms of funnel plot asymmetry (Figure 5).

3.2.2 Physical Health Related Quality of Life

Receiving any core component of CR improved physical HRQOL when compared to

usual care. Using 53 reports of 46 trials and 12,523 participants the SMC, with regard to

physical HRQOL was 0.23 (95% CI 0.08 to 0.38) (Figure 6). Using Cohen’s criteria, a small

treatment effect was observed. However, there was a considerable amount of clinical and

statistical heterogeneity when combining all the studies in the SMC model (I2

= 92%; 95% UI

89-95%). In relation to the I2 about 59% of the inconsistency observed was due to between

study variance with 33% due to within study variance. Random and fixed effect subgroup

analysis results of the SF-36/12 PCS, SF-36 physical functioning subscale, MacNew physical

well-being subscale and SAQ physical limitations subscale were inconsistent in showing an

improvement in physical HRQOL. The SF-36 physical functioning subscale and SAQ physical

limitation subscale had a SMC of 0.26 (95% CI 0.07 to 0.46), and 0.19 (95% CI 0.08 to 0.29)

32

respectively. All of which reported a small treatment effect size. The SF-36 PCS, MacNew and

SF-12 provided a SMC of 0.08 (95% CI - 0.02 to 0.19), 0.04 (95% CI -0.07 to 0.15) and 0.05

(0.00 to 0.11) respectively and provided no treatment effect. Table 4 provides an outline of the

overall summary effect and subgroup analysis of all the HRQOL instruments used to measure

physical HRQOL. The rank correlation test (tau = 0.2685) provided evidence of publication bias

(p = 0.004) for funnel plot asymmetry (Figure 7).

3.2.3 Emotional Health Related Quality of Life

Receiving any core component of CR did not improve emotional HRQOL when

compared to usual care. Using 42 reports of 39 trials and 11,539 participants the SMC, with

reference to emotional HRQOL was 0.11 (95% CI -0.03 to 0.24) (Figure 8). There was a

substantial amount of clinical and statistical heterogeneity when combining all the studies in the

SMC model (I2

= 86%; 95% UI 71-90%). Forty-one percent of the inconsistency seen in I2 was

due to between study variance, with 45% being due to within study variance. Random and fixed

effect subgroup analysis results of the SF-36/12 MCS, SF-36 emotional limitation subscale, and

the MacNew emotional well-being subscale were consistent in not showing an improvement in

emotional HRQOL. Table 5 provides an outline of the overall summary effect and subgroup

analysis of all the HRQOL instruments used to measure emotional HRQOL. The rank

correlation test (tau = 0.0395) provided no evidence of publication bias (p = 0.71) due to funnel

plot asymmetry (Figure 9).

3.2.4 Social Health Related Quality of Life

Receiving any core component of CR did not improve social HRQOL when compared to

usual care. Using 29 reports of 27 trials and 8,209 participants the SMC relating to social

HRQOL was 0.03 (95% CI -0.07 to 0.13) (Figure 10). There was a substantial amount of

33

clinical and statistical heterogeneity when combining all the studies in the SMC model (I2

=

75%; 95% UI 60-90%). With respect to the inconsistency seen in I2, 72% of the heterogeneity

was due to between study variance and only 3% due to within study variance. Random and fixed

effect subgroup analysis results of the SF-36 social functional scale and MacNew social well-

being subscale were shown to provide no treatment effect. Table 6 provides an outline of the

overall summary effect and subgroup analysis of all the HRQOL instruments used to measure

social HRQOL. The rank correlation test (tau = 0.0640) provided no evidence of publication

bias (p = 0.64) due to funnel plot asymmetry (Figure 11).

3.3 Meta-Regression

To explore the substantial heterogeneity seen in each HRQOL domain a meta-regression

was performed to examine the five a priori covariates: instrument type (generic, disease specific,

and preference), type of intervention (exercise, non-exercise, psychosocial only), year, duration

of follow-up and proportion of MI patients. Potential effect modifiers were entered into

univariable models to determine the percentage of heterogeneity explained by the covariates.

3.3.1 Overall Health Related Quality of Life

There was a substantial amount of clinical and statistical heterogeneity when combining

all the studies in the overall SMC model (I2

= 68%; 95% UI 44-87%). Only one of the

previously described variables explained any of the heterogeneity, which was duration of

follow-up explaining about 7% of the variance (Table 7). None of the covariates were shown to

be significant predictors of study effect sizes.

3.3.2 Physical Health Related Quality of Life

There was a considerable amount of heterogeneity when combining all the studies in the

physical SMC model (I2

= 92%; 95% UI 89-95%). Table 8 presents the estimates of the

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synthesized univariable meta-regression where the influence of instrument type, type of

intervention, year, duration of follow-up and proportion of MI patients was observed in regards

to HRQOL effect sizes. Type of intervention, type of instrument, year, duration of follow-up

and proportion of MI patients were not significant predictors of HRQOL effect sizes. None of

the examined covariates helped explain a significant amount of the between study heterogeneity

seen in the model.

3.3.3 Emotional Health Related Quality of Life

There was a considerable amount of clinical and statistical heterogeneity when

combining all the studies in the SMC model (I2

= 86%; 95% UI 74-91%). No covariates were

shown to be significant predictors of study effect sizes. Additionally, none of the examined

potential treatment effect modifiers explained a significant amount of between study

heterogeneity in the model. Only proportion of MI patients was shown to explain about 5% of

the unexplained variance.

3.3.4 Social Health Related Quality of Life

There was a substantial amount of clinical and statistical heterogeneity when combining

all the studies in the SMC model (I2

= 75%; 95% UI 60-90%). Only year explained any variance

(~6%). No potential treatment effect modifiers were shown to be significant predictors of study

effect sizes or shown to explain any significant amount of between study heterogeneity (Table

10).

4 Discussion

This systematic review and meta-analysis of RCTs examining CR interventions for CHD

patients was designed to determine if receiving any core component of CR was in general able

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to improve HRQOL. While CR has been previously shown to improve mortality in CHD

patients little research has been done on CRs ability to influence HRQOL domains because of

the heterogeneity in instruments and their varying psychometric properties. To our knowledge

this is one of the first studies to attempt to attain an overall summary effect of receiving any core

component of CR/secondary prevention program on HRQOL domains. The use of random

effects multilevel meta-analysis and univariable meta-regression helped clarify in general if

receiving any core component of CR was effective in improving overall, physical, emotional

and social HRQOL domains. Several important findings were discovered during this exploratory

analysis.

4.1 Health Related Quality of Life

Fifty-two trials with 13,360 adult CHD patients were included in order to determine

improvements in patients HRQOL following receiving any core component of CR. Receiving

any core component of CR resulted in a summary effect size of (SMC 0.14; 95% CI 0.03 to

0.25) and (SMC 0.23; 95% CI 0.08 to 0.38) for overall and physical HRQOL respectively.

While these effect sizes may be considered small treatment effects, they none the less represent

and incremental benefit in comparison to the usual standard of care (85). This improvement in

HRQOL domains was seen even though there is a considerable amount of variability in each

study because of differences in duration, delivery format, setting, population and intervention.

There was no difference seen between receiving any core component of CR and usual care

shown by the estimated effect sizes of the emotional (SMC 0.11; 95% CI - 0.03 to 0.24) and

social (SMC 0.03; 95% CI - 0.07 to 0.13) HRQOL domains. An effect size of zero demonstrates

that the treatment was not any different from the control and that there was no improvement in

HRQOL.

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When performing subgroup analysis on the different instruments used to assess each

HRQOL domain, no consistent treatment effects were seen. This was in part due to the

variability in patient types, interventions received, duration of follow-up and difference in

session lengths. Although some significant effects in each subgroup were observed they tended

to be in subgroups with small patient populations and did not involve many studies undermining

our confidence in the observed treatment effects. It is important to note that whilst some

domains were not statistically significant that does not reflect an absence of benefit altogether,

these HRQOL domains are not mutually exclusive and do influence one another. A non-

significant HRQOL outcome could reflect a lack of statistical power in the model to detect any

relevant change.

4.2 Meta Regression

Through the use of univariable random effects meta-regression we attempted to

investigate heterogeneity within the overall, physical, emotional and social HRQOL domains.

Each model had significant heterogeneity after pooling and the covariates defined a priori were

used to assess the proportion of variance explained. No statistically significant associations were

seen in any HRQOL domains using the potential effect modifiers type of type of intervention,

instrument type, year, duration of follow-up and proportion of MI patients. Additionally, the a

priori covariates did not explain much if any of the between study variance. Caution is needed

when interpreting our findings, especially in domains with low power. The lack of associations

discovered in our meta-regression should not take away from the possible importance of our

selected covariates in the use of explaining heterogeneity between CR studies in the future.

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4.3 Strengths of Study

One of the major strengths of this study was that we were able to examine and organize

an extensive amount of studies and instruments looking at the core components of CR to

determine whether their use provided an overall treatment effect on HRQOL. This was achieved

using a comprehensive search strategy which included nine electronic databases and the review

of reference lists