Department of Chemical Pathology, SydPath, St. Vincent's Hospital, Sydney, Darlinghurst, NSW, Australia;
Faculty of Medicine, University of New South Wales, Kensington, NSW, Australia.
Clin Chem. 2019 Apr;65(4):579-588. doi: 10.1373/clinchem.2018.290841. Epub 2019 Jan 28.
Within-subject biological variation data (CV) are used to establish quality requirements for assays and allow calculation of the reference change value (RCV) for quantitative clinical laboratory tests. The CV 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.
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 was then calculated by removing the effect of analytical variation.
This approach produced estimates of CV 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.
The process described here has allowed rapid and simple production of CV 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.
个体内生物学变异数据（CV）用于建立检测方法的质量要求，并允许计算定量临床实验室检测的参考变化值（RCV）。CV 通常是使用少量个体在受控条件下采集的大量样本确定的。本文提出的方法是使用从小部分个体（n=2）收集的常规临床目的的少量样本。
从常规病理数据库中提取 29 项常见化学和血液学检测的成人患者的成对连续结果。使用统计过程识别结果对比值的中心高斯分布，确定了 26 项测试的个体结果的总结果变异性。然后通过消除分析变异的影响来计算 CV。
该方法产生的 CV 估计值，对于本研究中的大多数分析物，与已发表的值具有良好的一致性。数据显示性别、年龄或样本之间时间的影响最小。分析物浓度显示出对分布的影响，第一个结果距离平均值越远，更有可能紧随其后的是接近平均值的结果。
这里描述的过程允许快速简单地生成 CV 数据。该技术不需要患者干预，复制了临床环境，尽管它可能不是普遍适用的。此外，这里描述的向均数回归的影响可能允许更好地解释连续患者结果。
