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Table. 1.
Comparison of traditional and next-generation PBRTQC methods
Considerations Traditional PBRTQC Next-generation PBRTQC
Underlying statistics Moving statistics involving data distribution, such as average, median, standard deviation, percentile, and positive results. This may include statistical weighting Regression adjustment, classification with a machine learning algorithm, AD, and hybrid statistical and machine learning methods
Input data for statistical algorithms Sequential patient results only Sequential patient results, patient demographic and clinical data, and instrument-related data
Extreme result/outlier exclusion strategy Patient subpopulation selection, statistical exclusion, winsorization Similar to traditional PBRTQC, but can be run without data exclusion
Hyperparameters need to be optimized Truncation limits, window size, and weights for weighted average methods Machine learning algorithm hyperparameters, random forest tree size and number of trees, deep learning network structure
Statistical parameter optimization Optimization through simulation Can be optimized through simulation, but using simulation may be problematic. More real-world out-of-control data are needed
Optimization outcome Number of patient results affected before error detection, false positive rate Similar to traditional PBRTQC; it can also include metrics, such as the area under the receiver operating characteristic curve
Other considerations Relatively simple, and the parameters and output are explainable. Lower computing resource requirements Commanding heavy computing resources, implementation is highly customized to individual laboratories

Abbreviation: AD, anomaly detection; PBRTQC, patient-based real-time QC.

Ann Lab Med 2024;44:385~391 https://doi.org/10.3343/alm.2024.0053

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