Exploring patient-based real-time quality control: Insights from EWMA and simulation modeling
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Furong Ying1, Zhichao Zhao2, Wei Chen3
1Department of Clinical Laboratory, Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
Background:
Accurate clinical laboratory testing is vital for patient safety. This study compares patient-based real-time quality control (PBRTQC) methods, focusing on the Exponentially Weighted Moving Average (EWMA) algorithm against the Moving Average (MA) and Moving Median (MM) algorithms to optimize quality control through parameter tuning.
Methods:
A simulation modeling study using nine months of historical data for 23 analytes evaluated PBRTQC performance. Data processing involved Box-Cox transformations to correct skewed distributions. Optimization targeted parameters like truncation limits, weight factors, and control limits.
Results:
Screening monitoring objects improved stability and reduced false alarms. Box-Cox transformations outperformed extreme value exclusion. Optimizing the EWMA smoothing parameter (λ) reduced false positives by >50% for 80% of analytes. EWMA showed superior sensitivity and specificity, especially for normally distributed data.
Conclusion:
PBRTQC methods can be optimized via simulations, enhancing clinical testing accuracy. Key optimization factors include monitoring object selection, data normalization, and parameter tuning.