1. Dr Bill Bartlett Joint Clinical Director. Diagnostics Group, Biochemical Medicine, Ninewells Hospital & Medical School, NHS Tayside, Scotland, UK. Bill.Bartlett@nhs.netwww.biologicalvariation.com

2.  Analytical variance (CV A ).  Within Subject biological variance (CV I ).  Between Subject biological variance (CV G ).   Total =   Analytical +   Individual +   Gro CV Total = CV A + CV l + CV G

3. Biological Variation Serum Creatinine: Average within subject (CVI) = 4.1% Gowans & Fraser. Ann Clin Biochem 1988:25:259-263

4.  Setting of analytical goals (CV goal ).  Quality specifications for :  total allowable error (TE A )  Bias (B A )  Evaluating the significance of change in serial results (RCV).  Assessing the utility of reference intervals (Index of Individuality).  Assessing number of specimens required to estimate homeostatic set points.  Choice of specimen type.  Timing of specimens.

5. eGFR > 60 in a 30 year old white female: Changing renal function?

6. These fundamental data have many applications that under-pin our practice! Rodin’s Thinker Burrell Collection Glasgow Are These Not Reference Data? Do we have confidence in the data and understand their limitations?

8. Are current published biological variation data fit for purpose?

9.  Are these data valid and robust?  Confidence in method of their production and analysis.  Contemporaneously valid.  Can I apply them to my practice?  Population  Demographic  Diseased v Well  Method  Time Frame  What are the implications of error

10. Grasbeck & Saris 1969 Introduced the term “reference value”: The mode of generation of such values is known with respect to: -  Selection of subjects  Assessment of state of health  Population characteristics, age, sex,  Specimen collection and storage  Analytical technique and performance characteristics  Data handling techniques.

11. 40 years of data Do the data travel through timeDo the data travel through time Method developmentsMethod developmentsQuality Enough reported detail.Enough reported detail. Good Design?Good Design? Commutable Population demographics.Population demographics. Healthy?Healthy? DiseasedDiseased? Translated into databases Excellent ResourcesExcellent Resources Granular enough?Granular enough? Data archetype required?Data archetype required? The Literature 319 Constituents: 90 entries based on 1 Paper 90 entries based on 1 Paper

12.  66 quantities 34 diseases with 45 references.  “For the majority of quantities studied CV I of same order as diseased. “  Disease specific RCVs may be necessary in some cases.  Effect of variability in variability not quantitatively studied.  “Heterogeneity in study designs and methods compiled”

13. What is the uncertainty? What are the quality standards for BV Data? Assay Characteristics Data Analysis Experimental Design Data Quality?

14. Experimental Design Data Analysis Standard for Production Enable Critical Appraisal Enable Commutability Standard for Reporting Data Archetype? Commutability & Valid Application Standard for Transmission

15. Our hope is that the comparability of such data might be provided by use of a common study design and analysis of data “ Our hope is that the comparability of such data might be provided by use of a common study design and analysis of data ” Fraser & Harris 1989 Crit Rev in Clin Lab Sci. 1989;27(5)409-437

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17. Generation and Application of data on Biological Variation in Clinical Chemistry: - Fraser CG, Harris EK. Crit Rev Clin Lab Sci 1989:27,(5), 409-435. Optimal Conditions Precision

19.  Purpose of study  Experimental Design  Characterisation of the methods  Data analysis  Confidence limits

20. 1. Define the purpose for which they are to be used. 2. Only meaningful and transferable if defined for the population or individual in terms of: -  Inclusion and exclusion criteria  Intake of food & drugs  Physiological and environmental conditions  Specimen collection criteria  Performance characteristics of the analytical method  The statistical methods used for estimation of the limits

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22. CV I 4% to 103% with central tertile 28% to 48% 40 studies with confounding factors: -  Time period over which samples were collected  Study design  Type of sample and concentration range studied  Population studied and state of health  Preanalytical factors  Poorly described statistical methods

23. Braga et al Clinica Chimica Acta 2010;411:1606-1610.  Highlights the need for this approach “Nine recruited studies were limited by choice of analytic methodology, population selection, protocol application and statistical analysis” Issues: -  Heterogeneity in experimental model  Length of study inappropriate (3 days to 6 months)  Methods with differing specificities  Statistical methods not specified

25.  These data have associated metadata that should remain associated with them to enable appropriate application.  Enable comutability  Analogous to a reference value.  Concept of Archetypes may be relevant

26. BV Data Population Demographic Measurement SNOMED-LOINC Disease SNOMED Published Reference PUBMED Method

27. ISSUES  Non-complex v complex molecules.  Relativity of normality.  Improved assay specificity.  HbA1c  PTH  Creatinine

28.  Data in chronic stable disease “often can be considered constant over time and geography”  “Same order of magnitude in disease and health”

29. Within Subject Variation (CV I,%) for Serum Sodium and Urea No. ofTimeSexstatusNa + Urea subjects subjects 110.5 hmH0.62.2 118 hmH0.56.0 621 dH0.64.8 112 weeksmH0.712.3 104 weeksmH0.914.3 148 weeksFH0.511.3 11115 weeksmH0.615.7 3722 weeksmH0.511.1 2746 months-H0.511.2 1540 weeks-H0.713.9 92 d-RF0.86.5 156 weeksFHP0.814.5 168 weeksmDM0.813.0 Fraser 2001

30. C-Terminal RIA 1970’s Development of IRMA assays 1980’s Nichols institute Ruled the world Range of other intact assays with antibodies against a variety of epitopes 1990’s Bioactive PTH Assays with n-terminal specific antibodies 2004 PTH Assays through time

31.  If clearance of fragments is not identical in all patients and non diseased patients the apparent biological variation will vary and be assay specific.  Assay specificity is an important BV qualifier  Historical data may not be always applicable.

32. StudyYearSubjects(M:F) State of Health Frequency of Sampling Number of Samples Method1198510(6:4)Healthy7 D 11 - 27IE 219898(?)Diabetic3-4 D6Endosmosis 3199373(?)Diabetic1 M & 3 M4Affin Chrom 4199429 (?)Diabetic3 M & 12 M?HPLC IE 5199812(7:5)Healthy15 D10HPLC IE 6200011(0:11)Healthy7 D5HPLC IE 7200047(?)Diabetic6 M 4 - 7Imm Turbid 8200245 (45:0)Diabetic7 D12HPLC Affin 9201038(24:14) a Diabetic1 Y5HPLC IE

33. Stud y CV I CV G Analytical Goal Desirable TE A (%) Bias Target RCV N for Homeostatic Setting point 1 H 1.80.94.8 1 2D 7.3 10. 83.65.82.822.6 10 3D 4.2 & 7.12.1 & 3.513.0 & 22.5 3 & 10 4D 2.41.27.4 1 5H 1.96.80.83.31.85.7 6H <0.7< 0.352.9 1 7D 7.9,5.4, 3.9 3.3 3.8,2.7, 1.81.40.8 24.3,16.7, 11.8 12,6,3 8D 1.7 b 0.8 9D 4.8 14.9 4 H = Healthy D = Diabetic

34.  Jaffe methods  Enzymatic methods  HPLC  ID-MS – reference method Review of the sedimentation process which is caused in normal urine by picric acid and a new reaction of creatinine By M. Jaffe (Submitted to the editor on 26th June 1886)

35.  Many points of reference.  International Standards

36. State of Health CV I Number of Subjects Length of Studies (days) Number Samples/Sub Healthy Median? 4.3 4.3 CRF5.317218 Type 1 DM 5.927568 Impaired renal function 6.99211 Type 1 DM 6.511568 Post renal transplant 11.541908 Acute MI 13.420419.5 CKD children 13.0545409 Ricos et al Ann Clin Biochem 2007;44: 343-352

37. QuantityUnitsGroupMeanCV I CV G Index of Individuality Serum Creatinine µmol/LMale (7)83.93.46.80.54Fraser µmol/LFemale (8)71.44.911.80.41Fraser * µmol/L * Whole (15)77.94.114.10.29Fraser µmol/L??5.314.20.4BioV Site ** µmol/L ** N= 20 Male (7) Female(13) 774.714.40.33Reinhard et al * Jaffe ** Enzymatic

38. CV I = 5.3 % CV G = 14.2%CV A =2.7%

39. MFG

40. Route Forward? www.biological variation.com

41. Need to assess on a case by case basis. Need to assess on a case by case basis. Questions around uncertainty. Questions around uncertainty. What are the implications for their application? What are the implications for their application? Can the impact of uncertainty be quantified and reduced where necessary. Can the impact of uncertainty be quantified and reduced where necessary. Accepted standard needed for their production. Accepted standard needed for their production. Critical appraisal checklist required to enable veracity of existing and new publications to be established. Critical appraisal checklist required to enable veracity of existing and new publications to be established. Archetype for transmission. Archetype for transmission. Questions to be addressed by the EFCC biological Variation Working group

42. Kinoull Hill, Perth Scotland. © Ruth Bartlett