Planning SQC strategies and adapting QC frequency for patient risk

Risk-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.

A 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.

We 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.

Run 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.