Planning SQC strategies and adapting QC frequency for patient risk

Abstract

BackgroundRisk-based Statistical QC strategies are recommended by the CLSI guidance for Statistical Quality Control (C24-Ed4). Using Parvin’s patient risk model, QC frequency can be determined in terms of run size, i.e., the number of patient samples between QC events. Run size provides a practical goal for planning SQC strategies to achieve desired test reporting intervals. MethodsA QC Frequency calculator is utilized to evaluate critical factors (quality required for test, precision and bias observed for method, rejection characteristics of SQC procedure) and also to consider patient risk as a variable for adjusting run size. ResultsWe illustrate the planning of SQC strategies for a HbA1c test where two levels of controls show different sigma performance, for three different HbA1c analyzers used to achieve a common quality goal in a network of laboratories, and for an 18 test chemistry analyzer where a common run size is achieved by changes in control rules and adjustments for the patient risk of different tests. ConclusionsRun size provides a practical characteristic for adapting QC frequency to systematize the SQC strategies for multiple levels of controls or multiple tests in a chemistry analyzer. Patient risk can be an important variable for adapting run size to fit the laboratory’s desired reporting intervals for high volume continuous production analyzers.