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Patient-based pre-classified real-time quality control with neural network (PCRTQC-NN)
Bo Zhou1,2, Xiaoying Li1,2, Shitong Cheng1,2
1National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Objectives:
Patient-based real-time quality control (PBRTQC) is essential for clinical laboratory management but struggles with detecting small systematic errors. This study presents the patient-based pre-classified real-time quality control with neural network (PCRTQC-NN) model, utilizing neural networks to improve error detection by extracting analytical features from testing instruments.
Methods:
Using PCRTQC's clustering analysis, we pre-classified and processed Na, CHOL, ALT, and CR data from 611,031 patients. A neural network autoencoder, trained using TensorFlow with mean squared error (MSE) as the loss function, extracted the testing instrument's analytical features under error-free conditions. Systematic errors were identified by comparing reconstruction residuals between test and reconstructed data. The average number of patient samples until error detection (ANPed) evaluated the model performance.
Results:
The PCRTQC-NN's error detection surpasses traditional algorithms Compared to PCRTQC, it reduced the ANPed for ALT by 37 % (constant error, CE) and 22 % (proportional error, PE) at 1 total error allowable (TEa), with comparable results for other analytes. For 0.5 TEa errors, the ANPed for CHOL decreased by 23 % (CE) and 22 % (PE), for ALT by 14 % (CE) and 6 % (PE), and for CR by 4 % (CE) and 9 % (PE), enhancing error detection capabilities for analytes with high inter-individual variability and sensitivity to smaller errors.
Conclusions:
PCRTQC-NN significantly enhances systematic error detection compared to PCRTQC, leveraging autoencoders to extract analytical features as discrete signals, thus improving SNR for high-variability analytes. It promises improved laboratory efficiency and inter-laboratory standardization via robust feature models. Future multi-center studies will validate broad applicability across diverse settings.
