Lund Flemming, Petersen Per Hyltoft, Fraser Callum G, Sölétormos György
Department of Clinical Biochemistry, North Zealand Hospital, University of Copenhagen, Hillerød, Denmark
Department of Clinical Biochemistry, North Zealand Hospital, University of Copenhagen, Hillerød, Denmark Norwegian Quality Improvement of Primary Care Laboratories (NOKLUS), Section for General Practice, University of Bergen, Bergen, Norway.
Ann Clin Biochem. 2015 Jul;52(Pt 4):434-40. doi: 10.1177/0004563214555163. Epub 2014 Sep 23.
Reference change values provide objective tools to assess the significance of a change in two consecutive results of a biomarker from an individual. However, in practice, more results are usually available and using the reference change value concept on more than two results will increase the number of false positive results.
A computer simulation model was developed using Excel. Based on 10,000 simulated measurements among healthy individuals, a series of up to 20 results of a biomarker from each individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial result. These successive serial differences were computed to give limits for significant bidirectional changes with constant cumulated maximum probabilities of 95% (p < 0.05) and 99% (p < 0.01).
From an individual factors used to multiply the first result were calculated to create limits for constant cumulated significant changes. The factors were shown to become a function of the number of results included and the total coefficient of variation.
The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.
参考变化值为评估个体生物标志物两个连续结果变化的显著性提供了客观工具。然而,在实际中,通常可获得更多结果,而将参考变化值概念应用于两个以上结果会增加假阳性结果的数量。
使用Excel开发了一个计算机模拟模型。基于健康个体的10000次模拟测量,利用个体内生物学变异加分析变异的不同值,为每个个体生成一系列多达20个生物标志物结果。将该系列中的每个新结果与初始结果进行比较。计算这些连续的序列差异,以给出具有95%(p < 0.05)和99%(p < 0.01)恒定累积最大概率的显著双向变化的限值。
计算用于乘以第一个结果的个体因素,以创建恒定累积显著变化的限值。结果表明,这些因素成为所包含结果数量和总变异系数的函数。
第一个结果应乘以适当的增减因素,以给出连续多次测量中显著双向变化的限值。