Regression-Adjusted Real-Time Quality Control

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

A study on quality control using delta data with machine learning technique

“…Recent studies by Xincen Duan, et al. [ 3 ] used an additional regression adjustment before using a common algorithm in the RARTQC framework removed autocorrelation in the test results, and allowed researchers to add additional variables, and to improve data transformation; Ichihara et al. [ 5 ] set up the weighted cumulative delta-check (wCDI) method, applying a series of techniques for data stability.…”

“…Xincen Duan et al. [ 3 ] used the residual of the regression model as the input for improving univariate statistical process control (SPC) algorithms. Ng et al [ 4 ].…”

“…Cembrowski GS, et al. [ 3 , 6 ] combined delta check (DC) and moving average (MA) algorithms, developed an average of deltas (AoD) strategy, used for monitoring the mean delta of consecutive, intra-patient results to detect systematic error. In their protocol, a simulated annealing algorithm was used to select the number of patient delta values to calculate the average delta and to determine truncation limits to eliminate the effect from large deltas.…”

Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

“…33 In the literature, different block sizes varying between 5 and 200 have been considered in different applications. 19,21,23,24,31,34 For instance, Fleming and Katayev 14 considered block sizes of 25, 50, 100, and 200, and determined a block size of 50 as optimal by using a goal of greater than 90% error detection rate in their simulation runs for the MA chart. However, it has also been concluded that optimal block size may be different in different studies.…”

“…A larger block size is more effective in detecting smaller sized shifts, while a smaller block size may be preferred in detecting larger sized shifts 33 . In the literature, different block sizes varying between 5 and 200 have been considered in different applications 19,21,23,24,31,34 . For instance, Fleming and Katayev 14 considered block sizes of 25, 50, 100, and 200, and determined a block size of 50 as optimal by using a goal of greater than 90% error detection rate in their simulation runs for the MA chart.…”