281 Automated Determination of Control Limits for Multi-Levey Jennings Plots

Introduction/Objective Quality control (QC) monitoring is a cornerstone of quality assurance in clinical laboratories. A mainstay of QC monitoring is the use of Levey-Jennings charts—introduced in 1950 as an adaptation of Shewhart’s statistical control charts used in industrial manufacturing. In these charts, consecutive assay results of QC materials are plotted over time, allowing for the detection of shifts, drifts, or outliers in repeated measures using well-established Westgard Rules or Six Sigma principles. A practical challenge in traditional QC monitoring is determining appropriate values for setting the mean and standard deviation control parameters, particularly in laboratories with extensive test menus and multiple analyzers performing the same test. Methods/Case Report We employ a variety of analytical and machine learning approaches—such as Gaussian curve fitting, moving averages, and unsupervised machine learning algorithms—to examine whether QC parameters can be generated analytically and automatically from live QC data. Results We remove outliers, adjust for instrument performance-related drifts, and account for reagent or QC material lot changes that would otherwise confound control limits calculated from noisy, uncurated data. Conclusion This work confirms that QC limit establishment and evaluation can be performed using more automated methods to help improve laboratory operations.