Planning SQC strategies and adapting QC frequency for patient risk
•The planning of risk-based SQC strategies is based on Parvin’s patient risk mode.•An electronic spreadsheet can be used to calculate QC frequency or run size.•Run size depends primarily on Sigma quality but is influenced by patient risk.•The desired reporting interval provides an operational goal f...
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Veröffentlicht in: | Clinica chimica acta 2021-12, Vol.523, p.1-5 |
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Sprache: | eng |
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Zusammenfassung: | •The planning of risk-based SQC strategies is based on Parvin’s patient risk mode.•An electronic spreadsheet can be used to calculate QC frequency or run size.•Run size depends primarily on Sigma quality but is influenced by patient risk.•The desired reporting interval provides an operational goal for run size.•Calculation of run size is critical for implementing the CLSI C24-Ed4 “roadmap”.
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. |
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ISSN: | 0009-8981 1873-3492 |
DOI: | 10.1016/j.cca.2021.08.028 |