biological variation, a practical review, dr c. ricos
DESCRIPTION
variabilidad biologicaTRANSCRIPT
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Biological Variation: a practical review
Carmen Rics
Brussels & Amsterdam
2010 Bio-Rad_QC Seminars
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Within-subjectbiological variation
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
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Fluctuation of the
concentration
of blody fluid components
around the setting point
Fraser CG. Biological Variation: from theory to practice. AACC press, 2001
Within-subjectbiological variation
Within-subjectbiological variation
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Between-subjectbiological variation
Between-subjectbiological variation
Differences in concentration
of the components of
biologic fluids
among persons
Fraser CG. Biological Variation: from theory to practice. AACC press, 2001
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How to estimate thecomponents of BV
How to estimate thecomponents of BV
Fraser CG. Biological Variation: from theory to practice. AACC press, 2001
1. To obtain n samples from m healthy volunteers n, m and sampling interval are irrelevant
Key factors: sample obtention and maintenance
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Rics C et al. Clin Chem 1994;40:472-477
2. To eliminate outliers Cochran test outlier values
Reed test outlier individuals
How to estimate thecomponents of BV
How to estimate thecomponents of BV
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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
How to estimate thecomponents of BV
How to estimate thecomponents of BV
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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
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What else?What else?
a DATABASE
selective
permanently updated
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Why?Why?
To give information on
quality specifications for
Imprecision (CV,%)
Bias (SE,%)
Total error (TE,%)
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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
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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
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Method (2)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
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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
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Method (3)Method (3)
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
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Results(Database, 2010 update)
319 analytes
213 papers (12 rejected)
182 authors (>15 countries)
59 journals
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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
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Database 2010 updateReferences
http:// www. Westgard.com/biodatabase1.htm
http:// www. seqc.es/es/Sociedad/51/102
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Database - contrasDatabase - contras
Discrepancies among authors in
some analytes (hormones)
A single paper available for 90
analytes
Many analytes not studied
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Database - prosDatabase - pros
Wide source of information
Papers poorly reliable have been
disegarded
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Database - Applications Database - Applications
Quality specifications
Delta check
Reference change value
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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
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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
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Use of Q specifications Use of Q specifications
2.2. to evaluate internal QC resultsto evaluate internal QC results
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Use of Q specifications Use of Q specifications
3.3. to evaluate EQA resultsto evaluate EQA results
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Use of Q specifications Use of Q specifications
3.3. to evaluate EQA resultsto evaluate EQA results
-- SEQCSEQC
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Use of Q specifications Use of Q specifications
3.3. to evaluate EQA resultsto evaluate EQA results
-- SEQCSEQC
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% of results reaching specifications based on BV
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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
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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
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Reference change valueReference change value
SOULD BE USED
For analytes with high individuality
CVI/CVG
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Reference change valueReference change value
SHOULD BE USED
In 276 of the 319 analytes
from the current database
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Reference change valueReference change value
RCV = 21/2*Zp*(CVA2 + CVW
2)1/2
RCV = 2.77 * (CVA2 + CVW
2)1/2
Fraser CG. Biological variation: from principles to practice. Washington DC. AACC Press ,2001
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Reference change vlaueReference change vlaue
Fraser CG. Biological variation: from principles to practice. Washington DC. AACC Press ,2001
Interpreting resultas of analytes with highindividuality
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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
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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
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Reference change value- two analytes combined
Reference change value- two analytes combined
-100
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100 U
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-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
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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.
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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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