Institut für Klinische Chemie und Pathobiochemie, Klinikum rechts der Isar der Technischen Universität München, München, Germany;
Institut für Klinische Chemie und Pathobiochemie, Klinikum rechts der Isar der Technischen Universität München, München, Germany.
Clin Chem. 2017 Aug;63(8):1377-1387. doi: 10.1373/clinchem.2016.269845. Epub 2017 Jun 14.
In clinical chemistry, quality control (QC) often relies on measurements of control samples, but limitations, such as a lack of commutability, compromise the ability of such measurements to detect out-of-control situations. Medians of patient results have also been used for QC purposes, but it may be difficult to distinguish changes observed in the patient population from analytical errors. This study aims to combine traditional control measurements and patient medians for facilitating detection of biases.
The software package "rSimLab" was developed to simulate measurements of 5 analytes. Internal QC measurements and patient medians were assessed for detecting impermissible biases. Various control rules combined these parameters. A -like algorithm was evaluated and new rules that aggregate Z-values of QC parameters were proposed.
Mathematical approximations estimated the required sample size for calculating meaningful patient medians. The appropriate number was highly dependent on the ratio of the spread of sample values to their center. Instead of applying a threshold to each QC parameter separately like the Westgard algorithm, the proposed aggregation of Z-values averaged these parameters. This behavior was found beneficial, as a bias could affect QC parameters unequally, resulting in differences between their Z-transformed values. In our simulations, control rules tended to outperform the simple QC parameters they combined. The inclusion of patient medians substantially improved bias detection for some analytes.
Patient result medians can supplement traditional QC, and aggregations of Z-values are novel and beneficial tools for QC strategies to detect biases.
在临床化学中，质量控制（QC）通常依赖于对照样品的测量，但由于缺乏可互换性等限制，这些测量结果难以检测到失控情况。患者结果的中位数也被用于 QC 目的，但可能难以区分观察到的患者人群中的变化与分析误差。本研究旨在结合传统的控制测量和患者中位数，以促进偏差的检测。
开发了软件包“rSimLab”来模拟 5 种分析物的测量。评估了内部 QC 测量和患者中位数，以检测不可允许的偏差。各种控制规则结合了这些参数。评估了类似于 - 的算法，并提出了聚合 QC 参数 Z 值的新规则。
数学逼近估计了计算有意义的患者中位数所需的样本量。所需的数量高度依赖于样本值的分散度与其中心的比值。与 Westgard 算法分别对每个 QC 参数应用阈值不同，所提出的 Z 值聚合平均这些参数。这种行为是有益的，因为偏差可能会对 QC 参数产生不同的影响，导致它们的 Z 变换值之间存在差异。在我们的模拟中，控制规则往往优于它们所组合的简单 QC 参数。患者结果中位数可补充传统 QC，聚合 Z 值是用于 QC 策略以检测偏差的新颖且有益的工具。
患者结果中位数可以补充传统的 QC，聚合 Z 值是用于 QC 策略以检测偏差的新颖且有益的工具。
