Biological Variation, A Practical Review, Dr C. Ricos

Biological Variation: a practical review

Carmen Rics

Brussels & Amsterdam

2010 Bio-Rad_QC Seminars

C Rics2010 QC Seminars

Within-subjectbiological variation


Fraser CG. Biological Variation: from theory to practice. AACC press, 2001

Age, sex

Diet, physic exercise

Pathology, treatment

Within-day variation, season variation

Homeostasis


Fluctuation of the

concentration

of blody fluid components

around the setting point





Between-subjectbiological variation

Between-subjectbiological variation

Differences in concentration

of the components of

biologic fluids

among persons



How to estimate thecomponents of BV



1. To obtain n samples from m healthy volunteers n, m and sampling interval are irrelevant

Key factors: sample obtention and maintenance


Rics C et al. Clin Chem 1994;40:472-477

2. To eliminate outliers Cochran test outlier values

Reed test outlier individuals




Rics C et al. Clin Chem 1994;40:472-477

3. To applicate the ANOVA test sI

2 =s (W+A)2 sA

2

sG2 = stotal

2 sI2 M1 M2 M3 Var

within-

subject

S1 Var s1

S2 Var s2

S3 Var s3

S4 Var s4

S(W+A)2




Compilation of data onBiological variation

Ross JW. Handbook of clinical chemistry. Boca Raton: CRC press, 1982:391-42

Fraser CG. Arch Pathol Lab Med 1988;112:404-15

Fraser CG. Arch Pathol Lab Med 1992;116:916-23

Sebastin-Gambaro et al. Eur J Clin ChemClin Biochem 1997;35:845-52


What else?What else?

a DATABASE

selective

permanently updated


Why?Why?

To give information on

quality specifications for

Imprecision (CV,%)

Bias (SE,%)

Total error (TE,%)



MaterialMaterial

1. PAPERS SEARCH: BIOS, CURRENT CONTENTS,

EMBASE, MEDLINE, PUBMED

2. CLASSIFICATION of the information obtained

- BV components CVW, CVG

- Calculations Individuality,

Critical differences

- Descriptions N, days, samples

- Observations Health status, fasting

Method (1)Method (1)

1. EXCLUSSIONS

Papers with too high analytical variation

(CVA> 0.5 CVW)

Papers not specifically designed to estimate CVWand CVG

Studies made within a day

Studies made on non-healthy subjects



2. EXPRESSION (for each analyte)

Papers in ascending order according to the CVW

Search for relationships between CVW and

number of subjects, sex, health status, fasting;

number of samples per subject, time span of the

study

If no relationships are observed: calculation of

the median of CVW and CVG values from all

papers compiled


Example: s- Glucose

CVW CVG CVA N Td Ss Mean Unit Year

4.2 10.8 2.4 40 28 3 5.5 mmol/L 19944.7 5.4 2.4 27 140 10 5.2 19894.7 6.1 2.1 14 70 10 5.3 19885.0 7.7 3.4 20 365 12 5.2 19895.5 7.8 2.5 68 112 11 94 mg/dL 19705.7 5.8 1.7 48 365 12 140 20026.5 2.7 1.6 9 70 10 94 19716.5 8.7 2.2 1105 60 9 4.8 mmol/L 19788.0 14.0 1.8 10 5 5 4.4 198610.4 NC 1.5 126 180 6 4.4 198513.1 3.2 3.0 10 5 5 4.8 199313.2 NC 1.5 148 180 6 4.0 1985

CVW CVG CVA N Td Ss Mean Unit Year

4.2 10.8 2.4 40 28 3 5.5 mmol/L 19944.7 5.4 2.4 27 140 10 5.2 19894.7 6.1 2.1 14 70 10 5.3 19885.0 7.7 3.4 20 365 12 5.2 19895.5 7.8 2.5 68 112 11 94 mg/dL 19705.7 5.8 1.7 48 365 12 140 20026.5 2.7 1.6 9 70 10 94 19716.5 8.7 2.2 1105 60 9 4.8 mmol/L 19788.0 14.0 1.8 10 5 5 4.4 198610.4 NC 1.5 126 180 6 4.4 198513.1 3.2 3.0 10 5 5 4.8 199313.2 NC 1.5 148 180 6 4.0 1985



3. CALCULATION OF SPECIFICATIONS

CVA(%) < 0.5 CVW

SEA (%) < 0.25 (CVW2 + CVG

2)1/2

TEA (%) < 1.65*CVA + SEA

- Elevitch FR editor. AP Conference II. Skokie IL 1976- Gowans EMSs et al. Scan J Clin Lab Invest 1988;48:757-764- Fraser CG et al. Scand J Clin Lab Invest 1993; 53 suppl 212:8-9


Results(Database, 2010 update)

319 analytes

213 papers (12 rejected)

182 authors (>15 countries)

59 journals


Database 2010 updateExample

Analyte Biological DesirableVariation Specifications

CVW CVG CV(%) SE(%) TE(%)Srm- -Amilase 8,7 28,3 4,4 7,4 14,6Srm- -Amilasa, pancreatic 11,7 29,9 5,9 8,0 17,7Srm- -Carotene 35,8 65,0 17,9 18,6 48,1Srm- -Fetoprotein 12,0 46,0 6,0 11,9 21,8Srm- -Tocoferol 13,8 15,0 6,9 5,1 16,5


Database 2010 updateReferences

http:// www. Westgard.com/biodatabase1.htm

http:// www. seqc.es/es/Sociedad/51/102


Database - contrasDatabase - contras

Discrepancies among authors in

some analytes (hormones)

A single paper available for 90

analytes

Many analytes not studied


Database - prosDatabase - pros

Wide source of information

Papers poorly reliable have been

disegarded


Database - Applications Database - Applications

Quality specifications

Delta check

Reference change value


Effect on clinical outcome

Effect on general clinic decisions

Professional recommendations

Regulatory bodies / EQAS proposals

Current state of the art

Effect on clinical outcome

Effect on general clinic decisions

Professional recommendations

Regulatory bodies / EQAS proposals

Current state of the art

Hyltoft P et al. Strategies to set global analytical quality specificationsin laboratory medicine. Scand J Clin Lab Invest 1999;57,7

Quality specificationsQuality specificationsStockholm international consensusStockholm international consensus

19991999


Hyltoft P et al. Strategies to set global analytical quality specificationsin laboratory medicine. Scand J Clin Lab Invest 1999;57,7

Use of Q specificationsUse of Q specifications

1.1. To design internal control ruleTo design internal control rule

To calculate the critical error increase CE = TEA / 1,96 CVA

To select the control procedure

CE

3

Rule Controls/run1:2s N=21:2,5s N=41:3s N=6

1:2s N=11:3 N=21:3,5s N=4

1;2,5s N=11:3s N=21:3,5s N=4


Use of Q specifications Use of Q specifications

2.2. to evaluate internal QC resultsto evaluate internal QC results



3.3. to evaluate EQA resultsto evaluate EQA results




-- SEQCSEQC




-- SEQCSEQC


% of results reaching specifications based on BV

Delta CheckDelta Check

Check < 2 * Zp (CVA2 +CVW

2)

Z = 1.96 significant autovalidation

Z = 2.58 highly significant manual verification

Fraser CG. Accred & Qual Assur 2002;7:455-460


Reference change valueReference change value

Difference between two consecutive

results that may indicate a change in

the patient health state

Fraser CG. Biological variation: from principles to practice. Washington DC. AACC Press ,2001



SOULD BE USED

For analytes with high individuality

CVI/CVG


SHOULD BE USED

In 276 of the 319 analytes

from the current database



RCV = 21/2*Zp*(CVA2 + CVW

2)1/2

RCV = 2.77 * (CVA2 + CVW

2)1/2



Reference change vlaueReference change vlaue


Interpreting resultas of analytes with highindividuality


Result Units Ref. values

Sodium 138 * mmol/L 135-147

Potassium 5.0 mmol/L 3.5-5.0

Urea 9.5 * * mmol/L 3.3-6.6

Creatinine 137 > mmol/L 50-100

Bilirubins 100 > > mmol/L NAME

Albumin 23 < < g/L 36-50

Calcium 2.27 * * mmol/L 2.1-2.6

Reference change value- reporting

Reference change value- reporting

NINEWELLS HOSPITAL AND MEDICAL SCHOOL

Fraser CG. Biological Variation: From Principles to Practice. Washington, DC, AACC Press, 2001


Reference change value- in pathology

Reference change value- in pathology

Pathology Analyte CVI (%)Cancer ovarium CA 125 46Cancer mamarian CA 15.3 17C. colorectal CEA 45Diabetesmellitus

HbA1C 9Microalbumin 36

Hepatic disease -fetoprotein 40Paget Alkaline phos. 12

Rics C et al. Ann Clin Biochem 2007; 44: 343352

Reference change value- two analytes combined

Reference change value- two analytes combined

-100

-50

0

50

100 U

r

a

t

o

s

D

i

f

e

r

e

n

c

i

a

s

(

%

)

-100 -50 0 50 100 150 Creatinina Diferencias (%)

estables i.r.aguda obstructiva toxicidad FK506

infeccin citomegalovirus rechazo agudo

VRC combinado

Biosca C. Clin Chem 2001;47:2146-8C Rics2010 QC Seminars

References (1)References (1)

Fraser CG. Biological Variation: From Principles to Practice. AACC Press, Washington DC, 2001.

Rics C, lvarez V, Cava F, Garca-Lario JV et al. Current databases on biological variation: pros,cons and progress. Scand J Clin Lab Invest 2004; 64: 17584.

Rics C, Iglesias N, Garca-Lario JV, Simn M et al. Within-subject biological variation in disease: collated data and clinical consequences. Ann Clin Biochem 2007; 44: 343352 .

Biosca C, Rics C, Jimnez CV, Lauzurica R et al. Are equally spaced specimen collections necessary to assess biological variation?. Evidence from renal transplant recipients. Clin Chim Acta 2000;301:79-85.


References (2)References (2)

Hyltoft Petersen P, Sandberg S, Fraser CG, Goldsmith H. Influence of index of individuality on false positives in repeated sampling from healthy individuals. Clin Chem Lab med 2001;391:160-165

Comit de garanta de la Calidad y Acreditacin de Laboratorios. Comisin de Calidad analtica. Base de datos de Variacin biolgica. Actualizacin del ao 2010. http://www.seqc.es/es/Sociedad/51/102

Fraser CG, Stevenson HP, Kennedy IMG. Biological variation data are necessary prerequisites for objective autoverification of clinical laboratory data. Accred Qual Assur 2002;7:455-460.

Biosca C, Rics C, Lauzurica R, Galimany R et al. Reference Change Value Concept Combining Two Delta Values to Predict Crises in Renal Posttransplantation. Clin Chem 2001;47:2146-8

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Biological Variation, A Practical Review, Dr C. Ricos