控制图
计算机科学
统计
回归
统计过程控制
自相关
质量(理念)
控制限值
质量管理
数据挖掘
回归分析
线性回归
数学
过程(计算)
机器学习
工程类
运营管理
管理制度
哲学
操作系统
认识论
作者
Xincen Duan,Beili Wang,Zhu Jing,Chunyan Zhang,Wen-An Jiang,Jia Zhou,Wenqi Shao,Zhang Yin,Qian Yu,Lei Luo,Kwok Leung Yiu,Kim Thiam Chin,Baishen Pan,Wei Guo
出处
期刊:Clinical Chemistry
[Oxford University Press]
日期:2021-09-06
卷期号:67 (10): 1342-1350
被引量:18
标识
DOI:10.1093/clinchem/hvab115
摘要
Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols.In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols.The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors.The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.
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