Patient-based pre-classified real-time quality control (PCRTQC)

Abstract

Background: Patient-based real-time quality control (PBRTQC) has gained increasing attention in clinical laboratory management. Although its valuable characteristics complement traditional quality control measures, its performance and practical application have faced scrutiny. In this study, patient-based pre-classified real-time quality control (PCRTQC), an extended approach was devised to enhance real-time quality control protocols.

Methods: PCRTQC distinguishes itself by incorporating an additional patient pre-classification step utilising the OPTICS algorithm, thus addressing interference from diverse patient types. The complete set of patient test results obtained from a clinical chemistry analyser at The First Hospital of China Medical University in 2021 was utilised. Constant error (CE) and proportional error (PE) were introduced as analytical errors. Four analytes were selected to evaluate the PCRTQC, measuring probability for false rejection (Pfr) and the average number of patient samples until error detection (ANPed). Relevant error detection charts were generated.

Results: The PCRTQC outperformed regression-adjusted real-time quality control (RARTQC) based on the ANPed by approximately 50% for both the CE and PE, compared to the RARTQC, particularly for the total allowable error threshold.

Conclusion: The pre-classification step effectively reduced inter-individual variation and improved data preprocessing, filtering, and modelling. The PCRTQC is a robust framework for real-time quality control research.

Keywords: Analytical errors; Laboratory management; PBRTQC; Quality control.