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Biological Variation

Biological Variation. Dr WA Bartlett Biochemical Medicine Ninewells Hospital & Medical School Dundee Scotland. Objectives. Identification the nature of biological variation. Appreciation of the significance of biological variation in clinical measurements.

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Biological Variation

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  1. Biological Variation Dr WA Bartlett Biochemical Medicine Ninewells Hospital & Medical School Dundee Scotland

  2. Objectives • Identification the nature of biological variation. • Appreciation of the significance of biological variation in clinical measurements. • Attain insight into the determination and application of indices of biological variation.

  3. Identification the nature of biological variation. What is meant by the term biological variation in the context of clinical biochemistry? A component of the variance in biochemical measurements determined by the physiology of the subjects observed.

  4. Components of Variance in Clinical Chemistry Measurements • Analytical variance. • Within Subject biological variance. • Between Subject biological variance.

  5. Biological Variation • All clinical chemistry measurements change with time. • Knowledge of temporal changes useful in diagnosis and interpretation. • Rate of change may be useful in prognosis. • Understanding of the sources of biological variation in non-diseased subjects is fundamental to the development of reference data.

  6. Sources of Biological Variation • Biological Rhythms (time) • Homeostasis • Age • Sex • Ethnicity • Pathology • Stimuli

  7. Practical significance of biological variation. • What is the significance of this result? • Is the performance of the analytical method appropriate (imprecision, accuracy)? • When should I measure it again? • Has this result changed significantly over time? • Changes in variability be used as a tool?

  8. Models of Biological Variation • Assume values represent random fluctuation around a homeostatic setting point. • More general model allows correlation between successive results. (Time series and non-decayed biological variation)

  9. Quantifying Biological Variation • How are you going to quantify biological variation? • You have to dissect out the components of variance: - • s2total = s2Analytical +s2Individual + s2Group

  10. Quantifying Biological Variation • s2Analytical = • s2Individual= • s2Group= Average variance of replicate assays within run analytical variance Average biological within subject variance. Average Variance around the homeostatic setting point Variance of true means among subjects. Variance in homeostatic setting points

  11. Quantifying Biological Variation • How do you do the experiment? • Subjects How many? • Collect specimensNumber? Frequency? • Analyse specimensMinimises2Analytical ? • Analyse dataOutliers? Statistics? • Apply results of analysis.

  12. Quantifying Biological Variation • Estimates of biological variation are similar regardless of: - • Number of subjects • Time scale of study (Short v Long?) • Geography • A lot of information can be obtained from small studies.

  13. Within Subject Variation (CVI,%) for Serum Sodium and Urea No. of Time Sexb status Na+ Urea subjects 11 0.5 h m H 0.6 2.2 11 8 h m H 0.5 6.0 62 1 d H 0.6 4.8 11 2 weeks m H 0.7 12.3 10 4 weeks m H 0.9 14.3 14 8 weeks F H 0.5 11.3 111 15 weeks m H 0.6 15.7 37 22 weeks m H 0.5 11.1 274 6 months - H 0.5 11.2 15 40 weeks - H 0.7 13.9 9 2 d - RF 0.8 6.5 15 6 weeks F HP 0.8 14.5 16 8 weeks m DM 0.8 13.0

  14. Collection of Specimens. • Conditions should minimise pre-analytical variables. • Healthy subjects. • Usual life styles. • No drugs (alcohol, smoking?). • Phlebotomy by same person. • Same time of day at regular intervals. • Set protocol for sample transport, processing & storage.

  15. Analysis of Specimens • Need to minimise analytical imprecision. • Ideal : - • Single lots of reagents and calibrants. • Single analyst and analytical system. • Single or very small number of batches.

  16. Preferred Protocol: Cotloveet al • Healthy subjects. • Specimens taken at set time intervals. • Specimens processed & stored frozen. • When ALL specimens are available: - Analysis of all samples in a single run. Simultaneous replicate analysis. Quality control to monitor drift

  17. Preferred Protocol: Cotloveet al • Advantage: - Minimisation of s2Analytical • Disadvantages: - Limits the number of specimens and subjects that can be studied. Analyte must be stable on storage.

  18. Other Protocols: Costongs et al • Collection and storage as before. • Singleton assay of all samples in a single run. • Duplicate assay of QC or patient pool to estimate s2Analytical

  19. Other Protocols: Costongs et al • Disadvantages: - • True estimate of s2Analytical ? • Integrity of QC materials • Viral infections of pools • Vial to vial variability in QC

  20. Other Protocols: Costongs/Moses et al • Samples assayed once or in duplicate on the day of collection • Disadvantage: - s2individual confounded by between batch variance. Advantage: - Useful if analyte is unstable.

  21. Analysis of Data • 2 Stages • Identification of outliers • Nested analysis of variance

  22. Applications of BV Data • Setting of analytical goals. • Evaluating the significance of change in serial results. • Assessing the utility of reference intervals. • Assessing number of specimens required to estimate homeostatic set points.

  23. Applications of BV Data • Assessment of reporting strategies. • Selecting the best specimen. • Comparing utility of available tests.

  24. Setting of analytical goals. • Accepted analytical goal for imprecision: - CVGoal = ½ CVI therefore: - CVAnalytical = CVGoal = ¼ of the s2Individual if achieved. (Harris. Am J Clin Pathol 1979:72;274)

  25. Utility of Analytical Goals • Assessment of methods and equipment. • Should be addressed in early stages of method development. • Index of Fiduciality: - CVAnalytical /CVGoal If <1 analytical goal met (Fraser Clin Chem 1988:34;995)

  26. Evaluating the significance of change in serial results. • Critical Difference or Reference Change value indicates the value by which 2 serial results must differ to be considered statistically significant: - CD = 2½ * Z * (CVA2 + CVI2)½ Probabilty = 95% Z = 1.96 Probability = 99% Z = 2.58 • Only valid if the variance of s2Individual is homogenous. (Costongs J Clin Chem Clin Biochem 1985;23:7-16)

  27. Multipliers for (CVA2 + CVI2) ½ to Obtain Critical • Difference at Different Levels of Probability • Multiplier 3.64 2.77 2.33 1.81 1.47 1.19 0.95 • (2 ½ * Z) • Probability of 0.01 0.05 0.10 0.20 0.30 0.40 0.50 • false alarm • Probability 99% 95% 90% 80% 70% 60% 50%

  28. Significance of Change? 63 year old patient: Cholesterol 1 = 6.60 mmol/L Cholesterol 2 = 5.82 mmol/L Significant change ? Cva = 1.6% CVI = 6.0% RCV = 2½ * Z * (CVA2 + CVI2)½ 95%RCV = 1.414 * 1.96 * (1.6 ½ + 6.60 ½) ½ = 17.2% 99%RCV = 1.414 * 2.58 * (1.6 ½ + 6.60 ½) ½ = 22.6% Actual Change = ((6.60 – 5.82)/6.60)*100= 11.8%

  29. Dispersion =Z* (SD2A + SD2I) • Dispersion of first result = result ± 1.96 SD : - • 95% level 6.60 = 5.80 –7.40 • 99% level 6.60 = 5.54 – 7.66 • Dispersion of 2 result • 95% level = 5.82 = 5.11 – 6.53 • 99% level = 5.82 = 4.89 – 6.75 • Overlap: therefore neither significantly or highly significantly different • Can use the formula to ascertain the probability that change is significant. Calculate Z using the (((6.6-5.82)/6.6)*100%) as RCV and look up in tables. 82% in this case.

  30. USE of RCV • Handbooks reports, 95% and 99% probabilities that change is significant. (> or >> * or **) • Delta checking, exemption reporting. • 95% auto validate, 99% refer for clinical validation or renanalysis.

  31. Index of Heterogeneity • Measure of the heterogeneity of variance within the study population: - ratio of the observed CV of the set of subjects variances (SDA+I2) to thetheoretical CV ( / 2/n-1) for the set. • The ratio should =1(1SD = 1/ /2n ) • Large ratio = more heterogeneity. • (Costongs J Clin Chem Clin Biochem 1985;23:7-16)

  32. Assessing the utility of reference intervals. • Utility of population based reference data? • Ratio of Within to Between subject variances. Index of Individuality = CVI / CVG Population Ref Intervals: - Index <0.6 = Limited in Value Index >1.4 = Applicable

  33. Biological Variation &Utility of Reference Intervals

  34. Number of specimens required to estimate homeostatic set points. • n = ( Z. CVA+I/D) • where: - • Z = number of Standard deviates for a stated probablity (e.g. 1.96 for 95%). • D = desired % closeness homeostatic set point.

  35. Number of specimens required to estimate homeostatic set points: - Cholesterol testing How many samples (n) required to estimate set point within ±5% given: - CVI = 4.9% CVA = 3% (Recommended) Substitute equation: - n = ( Z. CVA+I/D) n =[1.96·(32 + 4.92)½/5]2 = 5.07

  36. RCV at 95% and Number. of Specimens Required to Assess the Homeostatic Set Point at Different Levels of Imprecision CVACVI RCVa Number of (%)(%)(%) specimensb 2.0 4.7 14.1 4 3.0 4.7 15.4 5 4.0 4.7 17.1 6 5.0 4.7 19.0 7 6.0 4.7 21.1 9 7.0 4.7 23.4 11 8.0 4.7 25.7 13 9.0 4.7 28.1 16 10.0 4.7 30.6 19 15.0 4.7 43.5 38 20.0 4.7 56.9 65 aRCV (p <0.05) = 2.77 (CVA2 + CVI2)½, assuming no statistical evidence of heterogenity bNumber = mean result is within 5%of homeostatic set point1.962 x (CVA2 + CVI2) ½/25.

  37. Assessment of reporting strategies • Results may be reported in different formats e.g. 24h Urinary creatinine output: - CVI for concentration = 23.8% CVI for output per collection = 13.0% CD for concentration = 66.0% CD for output = 36.2%

  38. Selecting best Specimen. • e.g early morning urines for albumin versus 24h collections. • Random hormone measurements versus timed measurements.

  39. Comparing Available Tests • Creatinine v Creatinine Clearance • FT4 v TSH in replacement situations • FT4 v Total T4

  40. Reference Intervals Dr WA Bartlett Birmingham Heartlands & Solihull NHS Trust (Teaching)

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