Biological Variation: a practical review · 1. PAPERS SEARCH: BIOS, CURRENT CONTENTS, EMBASE,...

Biological Variation: a practical review

Carmen Ricós

Brussels & Amsterdam

2010 Bio-Rad_QC Seminars

C Ricós2010 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


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

2. To eliminate outliers� Cochran test outlier values

� Reed test outlier individuals




Ricós 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

• Sebastián-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 CVW

and 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 Desirable

Variation Specifications

CVW CVG CV(%) SE(%) TE(%)

Srm- αααα-Amilase 8,7 28,3 4,4 7,4 14,6

Srm- αααα-Amilasa, pancreatic 11,7 29,9 5,9 8,0 17,7

Srm- αααα-Carotene 35,8 65,0 17,9 18,6 48,1

Srm- αααα-Fetoprotein 12,0 46,0 6,0 11,9 21,8

Srm- αααα-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

<2

(2-3)

>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 Check�Delta Check

Δ Check < 2 ½ * Zp (CVA2 +CVW

2) ½

Z = 1.96 significant autovalidation

Z = 2.58 highly significant manual verification

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


�Reference change value�Reference 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<0.6




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 vlaue�Reference 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 46

Cancer mamarian CA 15.3 17

C. colorectal CEA 45

Diabetes

mellitus

HbA1C 9

Microalbumin 36

Hepatic disease α-fetoprotein 40

Paget Alkaline phos. 12

Ricós C et al. Ann Clin Biochem 2007; 44: 343–352

�Reference change value- two analytes combined

�Reference change value- two analytes combined

-100

-50

0

50

100 U

rato

s

Dife

ren

cia

s

(%)

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

estables i.r.aguda obstructiva toxicidad FK506

infección citomegalovirus rechazo agudo

VRC combinado

Biosca C. Clin Chem 2001;47:2146-8C Ricós2010 QC Seminars

References (1)References (1)

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

• Ricós C, Álvarez V, Cava F, García-Lario JV et al. Current databases on biological variation: pros,cons and progress. Scand J Clin Lab Invest 2004; 64: 175–84.

• Ricós C, Iglesias N, García-Lario JV, Simón M et al. Within-subject biological variation in disease: collated data and clinical consequences. Ann Clin Biochem 2007; 44: 343–352 .

• Biosca C, Ricós C, Jiménez 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

• Comité de garantía de la Calidad y Acreditación de Laboratorios. Comisión de Calidad analítica. Base de datos de Variación biológica. Actualización del año 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, Ricós 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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