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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

2019/01/29 by Graham Ross Dallas Jones, Graham Jones · 11 citations
Medicine · Immunology and Microbiology · Veterinary · #Clinical Laboratory Practices and Quality Control #Immunotoxicology and immune responses #Animal testing and alternatives

paper · pdf · doi:10.1373/clinchem.2018.290841

Abstract

Abstract BACKGROUND Within-subject biological variation data (CVI) are used to establish quality requirements for assays and allow calculation of the reference change value (RCV) for quantitative clinical laboratory tests. The CVI 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 CVI was then calculated by removing the effect of analytical variation. RESULTS This approach produced estimates of CVI 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 CVI 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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