Biological Variation The logical source for analytical goals

Transcription

1 Biological Variation The logical source for analytical goals Presented by: John Yundt-Pacheco Scientific Fellow Quality Systems Division Bio-Rad Laboratories

2 Acknowledgements Dr. Callum Fraser Clinical Leader of Biochemical Medicine Ninewells Hospital and Medical School Dundee Scotland Biological Variation: From Principles To Practice, AACC Press 2001 General strategies to set quality specifications for reliability performance characteristics. Scand J Clin Lab Invest.,1999 Vol Dr. Carmen Ricos Vall d Hebron General Hospital Vall d Hebron, Barcelona, Spain Ricos C et. al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest.,1999 Vol

3 Objectives Understand the hierarchy of strategies to set quality specifications Understand where biological variation based analytical goals come from Understand the difference between the minimum, desirable, and optimum biological variation analytical goals

4 The Need for Quality Specifications Quality specifications dictate the performance characteristics that must be realized in our test systems for them to satisfy their purpose. In the absence of quality specifications, there is no way to determine whether the control procedures being utilized are appropriate.

5 Which Quality Specifications? How does the non-expert know what quality specifications should be used? To answer this question, a conference was organized in Stockholm, Sweden in April 1999 by the IFCC, WHO and the International Union of Pure and Applied Chemistry (IUPAC). Participants with published papers on various quality specification models attended from 23 countries.

6 The Stockholm Conference Consensus Statement The Stockholm Conference ended with a consensus statement agreeing on a hierarchical model of quality specification desirability. This consensus statement and articles by the most influential conference participants was published in a special issue of The Scandinavian Journal of Clinical & Laboratory Investigation volume 59, no 7, November 1999.

7 The Quality Specification Hierarchy 1. Quality specifications in specific clinical situations 2. Quality specifications based on general clinical use of test results 3. Quality specifications from professional recommendations 4. Quality specifications based on regulation and external quality assessment.

8 The Quality Specification Hierarchy Continued 5. Quality specifications based on State of the Art. Not covered in the hierarchy is the wildly popular, inertia-based, implicit quality specification - as in We use the 1:2 s rule, er, because we always have

9 1 - Specific Clinical Situations The ideal error specification is based on assessing how analytical performance affects specific clinical decisions. The problem is that very little of this analysis has been done in a manner that lends itself to universal use. If results from are treated the same, but a 111 is treated differently, a 10% error tolerance is implied.

10 2 - General Clinical Situations Quality specifications based on the general clinical use of the results fall into to groups: Specifications based on Biological Variation Specifications based on the analysis of clinician s opinions

11 2b- Analysis of Clinicians Opinions Quality specifications can be derived by interviewing a number of clinicians about how they would interpret clinical results. A 63-year old man with high bloodpressure has a cholesterol value of 6.60 mmol/l. You advise him to change his lifestyle and diet. You review him after 2 months. What level of cholesterol would indicate to you that he took your advice?

12 Converting Opinions to Specifications Because this particular interview is about change over time of the results of a single individual, it can be used for determining a precision specification. The median of the differences between the responses and 6.60 can be converted into an analytical specification for precision.

13 3 - Professional Recommendations Various groups of experts have published quality specifications for specific sets of analytes. The National Cholesterol Education Panel in the US has published recommendations for the precision, accuracy and total allowable error for lipids. The American Diabetes Association has documented quality specifications for selfmonitoring blood glucose, etc.

14 4 - Regulatory Agencies Mandated performance goals set by regulation like CLIA 88 CLIA 88 specifies the Total Allowable Error for glucose as 10% at values 60 mg/dl or higher. Outside the US, proficiency programs have a variety of mechanisms for grading performance. They can all be translated into a analytical goal specification.

15 5 State of the Art State of the Art refers to deriving quality specifications by what is possible currently. An example of a State of the Art precision specification might be the median CV from a group of laboratories. State of the Art specifications can be derived from proficiency testing programs or inter-laboratory consensus programs.

16 inertia-based implicit specifications Quality specifications can be implicitly defined given the set of control rules in use. This is the most common form of quality specification used in the world, but it is clearly not optimal. Any of the previously discussed quality models is superior to an implicit quality specification.

17 Biological Variation Biological variation-based quality specifications are derived by evaluating the inherent biological variation of an analyte and determining how large imprecision and bias can be before they mask significant changes in the analyte.

18 Biological Variation Specifications Quality specifications based on biological variation have the following benefits: Firmly based on medical requirements Usable in all laboratories Generated using simple models Widely accepted

19 Components of Biological Variation Biological variation can be broken into two components: Within subject variation (CV w also referred to as CV I ) the normal amount of variation that is present in a human. Between subject variation (CV b also referred to as CV G ) the normal difference that is found between humans.

20 Illustration of Within Subject and Between Subject Biological Variation. Means and ranges of IgG concentrations in 12 healthy individuals. CV w = 4.5 CV b = 16.5 TE a = 9.5 Fraser CG, Harris E. Generation and Application of Data on Biological Variation in Clinical Chemistry. Crit Rev Clin Lab Sci 1989;27:420

21 Obtaining Biological Variation Data A database of biological variation data has been compiled by Dr. Carmen Ricos and her colleagues. They reviewed some 190 publications and compiled consensus values for CVw and CV B for 265 analytes. This database is published at and as well as other places.

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23 Where Glucose BV Values Came From Costongs GMPJ, Janson PCW, Bas BM. Short-term and long-term intra-individual variations and critical differences of clinical chemical laboratory parameters. J Clin Chem Clin Biochem 1985; 23: Davie SJ, Whiting KL, Gould BJ. Biological variation in glycated proteins. Ann Clin Biochem 1993; 30: Eckfeldt J,Chambless Ll, Shen Y. Short-term, Within-Person Variability in Clinician Chemistry test results.arch Pathol Lab Med 1994; 118: Fraser CG, Williams P. Short-term biological variation of plasma analytes in renal disease. Clin Chem1983;29: Fraser Callum G., Cummings Steven T., Wilkinson Stephen P., Neville Ronald G., Knox James D. E., Ho Olga, and MacWalter Ronald S.. Biological variability of 26 clinical chemistry analytes in elderly people.. Clin Chem 1989; 35: Godslang IF. Intra-individual variation: significant changes in parameters of lipid and carbohydrate metabolism in the individual andintra-individual variation in different test populations. Ann Clin Biochem 1985;22: 618:624

24 Where Glucose BV Values Came From Harris EK, Kanofsky P, Shakarji G and Cotlove E. Biological and analytic components of variation in longterm studies of serum constituents in normal subjects. Clin Chem 1970; 16: Juan-Pereira L. Variabilitat biologica intraindividual de les magnitudes bioquimiques. Aplicacions cliniques..doctoral Thesis, Barcelona University Ricós C, Codina R. La variabilidad biológica intraindividual como objeto de calidad analítica.. Rev Diag Biol 1989; 38: Ricos C, García-Arumí E, Rodriguiez-Rubio R, Schwartz S. Eficacia de un programa interno de controlde calidad. Quim Clin 1986; 5: Williams GZ, Widdowson GM and Penton J. Individual Character of Variation in Time-Series Studies of Healthy People II. Differences in Values for Clinical Chemical Analytes in Serum among Demographic Groups by Age and Sex. Clin Chem 1978; 24: Young DS, Harris EK and Cotlove E. Biological and Analytic Components of Variation in Long-Term Studies of Serum Constituents in Normal Subjects. Clin Chem 1971; 17: Rohlfing C, Wiedmeyer HM, Little R, Grota L, Tennill A, England J, Madsen R, Goldstein D. Biological variaiton of glycohemoglobin. Clin Chem 2002;48:

25 CV w and CV b as Quality Specifications Imprecision and Bias specifications can be derived from CV w and CV b with the following equations: Where 0.5 and are factors depending on the quality specification desired.

26 Ratio of CV A /CV I and Variation CV A /CV I Amount of Variation Added

27 Total Allowable Error Specification Total Allowable Error is the combination of the allowable imprecision multiplied by a z factor and the allowable bias:

28 Minimum, Desirable and Optimum Performance Specifications Three sets of performance specifications have been derived for biological variation values: 1. Minimum performance 25% increase 2. Desirable performance 12% increase 3. Optimum performance 3% increase Increase in CV I due to CV A

29 Minimum, Desirable and Optimum Factors Minimum uses 0.75 CV w Desirable uses 0.50 CV w Optimum uses 0.25 CV w

30 Z-scores and Probability Probability 99% 98% 97% 96% 95% 90% 85% 80% Unidirectional Bidirectional

31 Reference Change Value (1/3) RCV is the change in value over time that denotes a significant rise or fall in the concentration of the analyte measured RCV = Z * [(CV A ) 2 + (CV I ) 2 ] 1/2 With 2 results the variation is (variation of the first result 2 + variation of the second result 2 ) 1/2 For 2 results RCV = 2 1/2 + Z * [(CV A ) 2 + (CV I ) 2 ] 1/2

32 Reference Change Value (2/3) First value = 6.60 mmol/l Second value = 5.82 mmol/l Change = = 0.78 mmol/l Equivalent to (0.78/6.60) / 100 = 11.8% RCV = 2 1/2 + Z * [(CV A ) 2 + (CV I ) 2 ] 1/2 2 1/2 = Z = 1.96 for significant change or 2.58 for highly significant CV A = 1.6% CV I = 6%

33 Reference Change Value (3/3) Significant change RCV = * 1.96 * ( ) 1/2 = 17.2% Highly significant change RCV = * 2.58 * ( ) 1/2 = 22.6% Change of 11.8% is not significant.

34 Which specifications should be used? The Biological Variation databases are published using the Desirable specification. If these values are not obtainable, the Minimum specification should be used. There is little value in achieving better than Optimum performance.

35 Conclusions Consideration should be given to what quality specifications are used. Biological variation-based quality specifications are a very defensible and logical selection. Minimum, Desirable, and Optimum factors can be used to tune the biological variation specifications for values achievable and suitable for your laboratory.

36 Thank you!