Patient-based real-time quality control integrating neural networks and joint probability analysis

Objective: Patient-based real-time quality control (PBRTQC) utilizes patient test results to continuously monitor laboratory test quality, addressing issues like discontinuities and matrix effects of traditional internal quality control. However, its clinical performance still requires enhancement. This study combined neural networks (NN) and joint probability analysis (NN-PBRTQC) to improve the clinical performance of PBRTQC. Methods: Data were collected from Peking University Shenzhen Hospital and Nanfang Hospital Southern Medical University, which included a series of analytes. A neural network model was trained to predict the test results by integrating patient demographics. Residuals between the expected and actual test results were inputs for statistical process control algorithms to monitor analytical errors. Additionally, an intelligent alarm system using joint probability analysis was developed to reduce the false alarm rate (FAR). The performance of NN-PBRTQC was evaluated using FAR, and the number of patients until error detection was compared to traditional PBRTQC. Results: NN-PBRTQC significantly enhanced the clinical performance of PBRTQC. Under the same desired FAR (DFAR) of 0.1 %, NN-PBRTQC required 64 % fewer samples for error detection than traditional PBRTQC for the analytes, which improved the sensitivity of error detection. Conclusion: NN-PBRTQC provides a novel method for PBRTQC, effectively addressing sample variations and false alarms. It significantly reduces the false alarm rate and the sample size required for error detection, accelerating the implementation of PBRTQC in laboratories.