Biological Variation: a practical review · 1. PAPERS SEARCH: BIOS, CURRENT CONTENTS, EMBASE,...
Transcript of 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
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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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Ricós 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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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
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
• Sebastián-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
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
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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 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
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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
<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
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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
�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
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�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
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�Reference change value�Reference change value
SOULD BE USED
• For analytes with high individuality
CVI/CVG<0.6
Fraser CG. Biological variation: from principles to practice. Washington DC. AACC Press ,2001
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�Reference change value�Reference change value
SHOULD BE USED
• In 276 of the 319 analytes
from the current database
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�Reference change value�Reference 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 vlaue�Reference 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 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.
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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
• 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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