A multi-test planning model for risk based statistical quality control strategies

• Published in: Clinica chimica acta 2021-12, Vol.523, p.216-223
• Main Authors: Westgard, Sten A., Bayat, Hassan, Westgard, James O.
• Format: Article
• Language: English
• Subjects: Frequency of QC; Humans; Multi-rule QC; Multi-stage QC; Multi-test analyzer; QC rules; QC schedule; Quality Control; Risk based SQC strategy; Run size; Statistical Quality Control
• ISSN: 0009-8981; 1873-3492
• DOI: 10.1016/j.cca.2021.09.020

•Guidance is provided for developing SQC strategies for multi-test systems.•A detailed planning model is described and demonstrated for 3 multi-test systems.•The key parameter is QC frequency, defined in terms of run size.•Calculation of run size by electronic spreadsheets facilitates the process.•The operational goal is to match run size with the desired reporting interval. Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected number of erroneous patient test results that would be reported over the period an error condition goes undetected. A planning model is described to provide a structured process for considering critical variables for the development of SQC strategies for continuous production multi-test analytic systems. The model aligns with the principles of the CLSI C24-Ed4 “roadmap” and calculation of QC frequency, or run size, based on Parvin’s patient risk model. Calculations are performed using an electronic spreadsheet to facilitate application of the planning model. Three examples of published validation data are examined to demonstrate the application of the planning model for multi-test chemistry and enzyme analyzers. The ability to assess “what if” conditions is key to identifying the changes and improvements that are necessary to simplify the overall system to a manageable number of SQC procedures. The planning of risk based SQC strategies should align operational requirements for workload and reporting intervals with QC frequency in terms of the run size or the number of patient samples between QC events. Computer tools that support the calculation of run sizes greatly facilitate the planning process and make it practical for medical laboratories to quickly assess the effects of critical variables.