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Related Experiment Videos

Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New Clinical and

Hassan Bayat1, Sten A Westgard2, James O Westgard2,3

  • 1Sina Medical Laboratory, Qaem Shahr, Iran.

The Journal of Applied Laboratory Medicine
|July 8, 2020
PubMed
Summary

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New guidelines recommend risk-based statistical quality control (SQC) strategies. This study provides a practical run size nomogram to help laboratories optimize SQC frequency and minimize unreliable patient results.

Area of Science:

  • Clinical laboratory science
  • Statistical quality control
  • Medical diagnostics

Background:

  • Clinical and Laboratory Standards Institute (CLSI) guideline C24-Ed4 promotes risk-based statistical quality control (SQC).
  • This guideline aligns with the patient risk model in CLSI EP23A and suggests optimizing SQC frequency based on expected unreliable patient results.
  • A key limitation is the lack of practical tools for laboratories to implement and verify these risk-based SQC strategies.

Purpose of the Study:

  • To develop practical tools for implementing risk-based statistical quality control (SQC) strategies.
  • To provide laboratories with methods for optimizing SQC frequency.
  • To address the need for tools to verify risk-based SQC applications.

Main Methods:

  • Utilized power curves to characterize SQC procedure rejection characteristics.

Related Experiment Videos

  • Employed Parvin's MaxE(Nuf) parameter to predict the risk of erroneous patient results.
  • Calculated run size based on MaxE(Nuf) and its relation to the probability of detecting critical systematic error (Pedc).

    • Main Results:

    • Developed a run size versus Pedc plot serving as a nomogram.
    • The nomogram facilitates estimation of run size for common single-rule and multirule SQC procedures.
    • Demonstrated a method for estimating run size for Ns of 2 and 4.

    Conclusions:

    • The traditional SQC selection process can be extended to determine SQC frequency using a run size nomogram.
    • Practical tools like the developed nomogram are essential for planning effective risk-based SQC strategies.
    • This approach aids in optimizing laboratory quality control and minimizing patient risk.