A Comparative Study of the Clinical Laboratory Quality Control Performance of AI-PBRTQC and Traditional PBRTQC Model...

Background: The accuracy of tumor marker testing is critical for clinical decision-making. Patient-based real-time quality control (PBRTQC), as a complementary approach to traditional internal quality control (IQC), has been widely adopted in clinical laboratories. With the rapid advancement of automation and artificial intelligence (AI) in recent years, a large number of AI-based PBRTQC optimization algorithms have emerged. This study compared Patient-based real-time quality control integrating neural networks and joint probability analysis (NN-PBRTQC), Patient-Based Pre-Classified Real-Time Quality Control (PCRTQC), and traditional PBRTQC to identify the optimal method for quality control of tumor marker testing. Methods: The study utilized clinical tumor marker testing data from Peking University First Hospital. Six common tumor markers were selected, and constant error (CE) and proportional error (PE) were introduced as measures of analytical error. The False Alarm Rate (FAR) was used to reflect the specificity of the algorithms, while the Trimmed Average Number of Patient Results Affected Before Error Detection (tANPed) was used to reflect their sensitivity, in order to compare the clinical performance of the different models. Results: Under the same desired FAR (DFAR) of 0.1%, NN-PBRTQC reduced the tANPed for the six tumor markers by an average of 62% compared to the traditional PBRTQC while maintaining the same FAR, which demonstrated superior sensitivity of error detection. Meanwhile, although PCRTQC strictly controlled the FAR, its tANPed was 23% higher on average than that of the traditional PBRTQC, which indicated insufficient sensitivity of error detection. Conclusions: NN-PBRTQC demonstrated superior comprehensive quality control performance in the comparison of six common tumor markers. While ensuring that the FAR does not deviate from the DFAR, it significantly reduces tANPed, such that it could meet the specificity and sensitivity requirements of clinical testing. It is expected to enable more efficient and accurate detection of tumor marker errors.