Westgard J O, Smith F A, Mountain P J, Boss S
Department of Pathology and Laboratory Medicine, University of Wisconsin Medical School, Madison 53792, USA.
Clin Chem. 1996 Oct;42(10):1683-8.
Achieving high quality and high productivity with automated testing processes will require process control systems that are optimized for the necessary error detection, minimum false rejection, and maximum run length. This study investigates whether run length could be monitored by average of normals (AON) algorithms that truncate the patient test distribution and estimate the average of a suitable number of patient results. The design of AON algorithms for individual analytes is facilitated by computer-simulated power curves that consider the ratio of the population biological variation (Spop) to the test method variation (Smeas), represent a range of Spop/Smeas ratios from 2 to 15, and include numbers of patient test results from 10 to 600. The potential applications of AON algorithms are assessed for 38 tests whose quality requirements represent the total error criteria from the Ontario Medical Association Laboratory Proficiency Testing Program, Spop/Smeas ratios from 0 to 32, critical systematic shifts from 0.02 to 10.85 Smeas, and test workloads representative of a regional reference laboratory. Approximately half of these tests provide high potential for applying AON algorithms to monitor run length.
要通过自动化测试流程实现高质量和高生产率，将需要针对必要的错误检测、最小误拒收率和最大运行长度进行优化的过程控制系统。本研究调查了运行长度是否可以通过截断患者测试分布并估计适当数量患者结果平均值的正态均值（AON）算法来监测。通过计算机模拟的功效曲线来辅助设计针对单个分析物的AON算法，这些曲线考虑了总体生物学变异（Spop）与测试方法变异（Smeas）的比率，代表了从2到15的一系列Spop/Smeas比率，并包括从10到600的患者测试结果数量。针对38项测试评估了AON算法的潜在应用，这些测试的质量要求代表了安大略省医学协会实验室能力验证计划的总误差标准、从0到32的Spop/Smeas比率、从0.02到10.85 Smeas的关键系统偏移以及代表区域参考实验室的测试工作量。这些测试中约有一半具有应用AON算法监测运行长度的高潜力。
