Biological Variation: a practical review Carmen Ricós Brussels & Amsterdam 2010 Bio-Rad_QC Seminars

C Ricós 2010 QC Seminars

Within-subject biological variation Age, sex Diet, physic exercise Pathology, treatment Within-day variation, season variation • Homeostasis • • • •

Fraser CG. Biological Variation: from theory to practice. AACC press, 2001 C Ricós 2010 QC Seminars

Within-subject biological variation Fluctuation of the concentration of blody fluid components around the setting point

Fraser CG. Biological Variation: from theory to practice. AACC press, 2001 C Ricós 2010 QC Seminars

Between-subject biological variation Differences in concentration of the components of biologic fluids among persons Fraser CG. Biological Variation: from theory to practice. AACC press, 2001 C Ricós 2010 QC Seminars

How to estimate the components of BV 1. To obtain n samples from m healthy

volunteers
n, m and sampling interval are irrelevant
Key factors: sample obtention and maintenance

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

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How to estimate the components of BV 2. To eliminate outliers
Cochran test
Reed test

outlier values outlier individuals

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

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How to estimate the components of BV 3. To applicate the ANOVA test
sI2 =s (W+A)2 – sA2
sG2 = stotal2 – sI2

M1 M2

M3

Var withinsubject

S1

Var s1

S2

Var s2

S3

Var s3

S4

Var s4 S(W+A)2

Ricós C et al. Clin Chem 1994;40:472-477 C Ricós 2010 QC Seminars

Compilation of data on Biological 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 Chem Clin Biochem 1997;35:845-52

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What else? a DATABASE

• selective • permanently updated

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Why? To give information on quality specifications for • Imprecision (CV,%) • Bias (SE,%) • Total error (TE,%)

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Material 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 C Ricós 2010 QC Seminars

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

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Method (2) 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 C Ricós 2010 QC Seminars

Example: s- Glucose CVW CVG CVA N 4.2 4.7 4.7 5.0 5.5 5.7 6.5 6.5 8.0 10.4 13.1 13.2

10.8 5.4 6.1 7.7 7.8 5.8 2.7 8.7 14.0 NC 3.2 NC

2.4 2.4 2.1 3.4 2.5 1.7 1.6 2.2 1.8 1.5 3.0 1.5

Td

40 28 27 140 14 70 20 365 68 112 48 365 9 70 1105 60 10 5 126 180 10 5 148 180

Ss Mean Unit

Year

3 10 10 12 11 12 10 9 5 6 5 6

1994 1989 1988 1989 1970 2002 1971 1978 1986 1985 1993 1985

5.5 5.2 5.3 5.2 94 140 94 4.8 4.4 4.4 4.8 4.0

mmol/L

mg/dL

mmol/L

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Method (3) 3. CALCULATION OF SPECIFICATIONS • CVA(%) < 0.5 CVW • SEA (%) < 0.25 (CVW2 + CVG2)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 C Ricós 2010 QC Seminars

Results (Database, 2010 update)

319 analytes 213 papers (12 rejected) 182 authors (>15 countries) 59 journals C Ricós 2010 QC Seminars

Database 2010 update Example

Analyte

Biological

Desirable

Variation

Specifications

CVW

CVG

CV(%)

SE(%)

TE(%)

Srm-

α-Amilase

8,7

28,3

4,4

7,4

14,6

Srm-

α-Amilasa, pancreatic

11,7

29,9

5,9

8,0

17,7

Srm-

α-Carotene

35,8

65,0

17,9

18,6

48,1

Srm-

α-Fetoprotein

12,0

46,0

6,0

11,9

21,8

Srm-

α-Tocoferol

13,8

15,0

6,9

5,1

16,5

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Database 2010 update References

http:// www. Westgard.com/biodatabase1.htm http:// www. seqc.es/es/Sociedad/51/102

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Database - contras

• Discrepancies among authors in some analytes (hormones) • A single paper available for 90 analytes • Many analytes not studied

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Database - pros

•

Wide source of information

•

Papers poorly reliable have been disegarded

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Database - Applications

 Quality specifications  Delta check  Reference change value

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Quality specifications Stockholm international consensus 1999

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 specifications in laboratory medicine. Scand J Clin Lab Invest 1999;57,7 C Ricós 2010 QC Seminars

Use of Q specifications 1. To design internal control rule • •

To calculate the critical error increase ∆CE = TEA / 1,96 CVA To select the control procedure ∆CE 3

1;2,5s 1:3s 1:3,5s

N=1 N=2 N=4

Hyltoft P et al. Strategies to set global analytical quality specifications in laboratory medicine. Scand J Clin Lab Invest 1999;57,7 C Ricós 2010 QC Seminars

Use of Q specifications 2. to evaluate internal QC results

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Use of Q specifications 3. to evaluate EQA results

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Use of Q specifications 3. to evaluate EQA results - SEQC

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Use of Q specifications 3. to evaluate EQA results - SEQC % of results reaching specifications based on BV

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Delta Check Δ Check < 2 ½ * Zp (CVA2 +CVW2) ½ Z = 1.96 significant Z = 2.58 highly significant

autovalidation manual verification

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

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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

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Reference change value SOULD BE USED • For analytes with high individuality CVI/CVG >>