Assessment of patient-based real-time quality control algorithm performance on different types of analytical error

Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to detect analytical errors in situations wherein internal quality control is less effective. Multiple PBRTQC algorithms have been proposed. However, there is a lack of comprehensive comparison of the performance of PBRTQC algorithms on different types of analytical errors. Thus, a comparative study was conducted.

The performance of six different PBRTQC algorithms was evaluated on three types of analytical errors using 906,552 test results for outpatient serum sodium, chloride, alanine aminotransferase, and creatinine at the Department of Laboratory Medicine at Zhongshan Hospital, Fudan University in 2019. The performance results were compared and assessed.

The moving average, moving median, exponentially weighted moving average, and moving quartiles performed similarly for effectively detecting constant errors (CE) and proportional errors (PE) but not random errors (RE). The moving sum of positive patients and moving standard deviation could detect RE for serum sodium and chlorides but performed poorly on detecting the CE and PE.

This study demonstrated the importance of assessing the potential source of error of a particular analyte and the corresponding type of analytical error before choosing a quality control algorithm for implementation.