OBJECTIVE The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. METHOD Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. RESULTS The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. CONCLUSION A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors.

中文翻译：

---

CUSUM-Logistic 回归分析，用于快速检测临床实验室测试结果中的错误。


目的 质量控制 (QC) 材料定期分析的主要缺点是，在 QC 分析之间的时间段内未监测测试性能，可能导致报告错误的测试结果。本研究的目的是开发基于患者的质量控制程序，以便更及时地检测测试错误。方法 使用 Beckman LX20 分析仪测量的 Chem-14 面板的结果来开发模型。每项测试结果都是通过多元回归从小组的其他 13 名成员中预测出来的，这导致 14 项测试中有 8 项的预测结果和测量结果之间的相关系数为 >0.7。然后开发了一个逻辑回归模型，该模型利用测量的测试结果、预测的测试结果、星期几和一天中的时间来预测测试误差。逻辑回归的输出通过每日 CUSUM 方法进行统计，并用于预测测试误差，固定特异性为 90%。结果 CUSUM-Logistic 回归 (CSLR) 错误检测前的平均游程长度 (ARL) 为 20，平均灵敏度为 97%，比简单预测模型的平均 ARL 53（灵敏度 87.5%）短得多仅使用测量结果进行错误检测。结论 对患者实验室数据进行 CUSUM-Logistic 回归分析可以成为快速、灵敏地检测临床实验室错误的有效方法。