Quality control optimization part II: A method to optimize the accuracy of laboratory quality control

Abstract

BackgroundQuality control (QC) can be viewed as a diagnostic test that is used to determine whether an assay is in statistical control. Using this framework, QC performance can be evaluated using familiar metrics associated with diagnostic tests. QC plan parameters can be adjusted to optimize performance metrics. MethodsWe developed a simple dichotomous model based on classification of assay errors. Errors are classified as important or unimportant based on a critical shift size, defined as Sc. Using this scheme, we show how QC policies can be analyzed using common accuracy metrics such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We conducted computer experiments to determine the QC plan that optimizes QC accuracy under a wide range of scenarios. ResultsIn general, traditional QC plans (based on2 or 3 standard deviation limits) are approximately 90% as accurate as optimized QC limits in the scenarios that were tested. There are special circumstances when traditional QC plans do not perform well. ConclusionQC performance can be optimized for specific contexts.