Estimates of Within-Subject Biological Variation Derived from Pathology Databases: An Approach to Allow Assessment of the Effects of Age, Sex, Time between Sample Collections, and Analyte Concentration on Reference Change Values

Clinical Chemistry: Journal of the American Association for Clinical Chemists

Graham Ross Dallas Jones^1,2¹ 

[1]Department of Chemical Pathology, SydPath, St. Vincent's Hospital, Sydney, Darlinghurst, NSW, Australia^1

[2]Faculty of Medicine, University of New South Wales, Kensington, NSW, Australia.^2

DOI  :  10.1373/clinchem.2018.290841

学科分类：过敏症与临床免疫学

来源: American Association for Clinical Chemistry

【 摘 要 】

BACKGROUND: Within-subject biological variation data (CV_I) are used to establish quality requirements for assays and allow calculation of the reference change value (RCV) for quantitative clinical laboratory tests. The CV_I is generally determined using a large number of samples from a small number of individuals under controlled conditions. The approach presented here is to use a small number of samples (n = 2) that have been collected for routine clinical purposes from a large number of individuals. METHODS: Pairs of sequential results from adult patients were extracted from a routine pathology database for 29 common chemical and hematological tests. Using a statistical process to identify a central gaussian distribution in the ratios of the result pairs, the total result variation for individual results was determined for 26 tests. The CV_I was then calculated by removing the effect of analytical variation. RESULTS: This approach produced estimates of CV_I that, for most of the analytes in this study, show good agreement with published values. The data demonstrated minimal effect of sex, age, or time between samples. Analyte concentration was shown to affect the distributions with first results more distant from the population mean more likely to be followed by a result closer to the mean. DISCUSSION: The process described here has allowed rapid and simple production of CV_I data. The technique requires no patient intervention and replicates the clinical environment, although it may not be universally applicable. Additionally, the effect of regression to the mean described here may allow better interpretation of sequential patient results.

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