Background: Patient-based real-time quality control (PBRTQC) is a valuable tool for monitoring the performance of testing processes. We aimed to compare and optimize various PBRTQC procedures for serum sodium.
Methods: In a computer simulation, artificial errors were added to 680,000 real patients' results. The characteristics of error detection of various algorithms-moving average, moving median, moving SD and moving proportion of normal results including different control limits (CLs)-were assessed on their ability to detect critical errors early.
Results: The moving average and moving median were sensitive to system error, and the moving SD tended to detect random error. P3SD (moving proportion of normal results, CLs based on mean and SD of proportion of normal results) demonstrated excellent performance for both system error and random error. The increase of block sizes (N) leads to the delay of error detection and the decrease of false rejection, except for QC procedures with minimum and maximum as CLs. CLs calculation with "0.1% false alarm rate" had more effective performance than that set false alarm to zero (minimum and maximum as CLs). The impact of truncation on QC performance depended on truncation limits, algorithms and the types of error. The significant improvement in QC performance due to truncation was only found in moving SD.
Conclusion: "P3SD ,N = 50, without truncation" and "moving SD, N = 25, set 0.1% false alarm as CLs and set 1% outliers exclusion as truncation limits" were recommended as the optimized procedures for serum sodium to monitor system error and random error, respectively.
Keywords: moving average; moving median; moving proportion of normal results; moving standard deviation; patient-based real-time quality control.
© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.