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Related Experiment Videos

Matrix effects and accuracy assessment. Identifying matrix-sensitive methods from real-time proficiency testing data

N S Lawson1, T L Williams, T Long

  • 1Department of Pathology, St John Hospital and Medical Center, Detroit, MI.

Archives of Pathology & Laboratory Medicine
|April 1, 1993
PubMed
Summary
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Proficiency testing data can identify matrix effects by detecting methodologic bias. High unacceptability rates linked to bias warrant further investigation into potential matrix interference in laboratory testing.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Analytical Chemistry

Background:

  • Proficiency testing (PT) data offers a valuable resource for identifying potential matrix effects in laboratory assays.
  • Matrix effects can manifest as methodologic bias, leading to inaccurate test results.
  • High unacceptability rates in PT programs may indicate underlying issues with assay methodology or sample matrix interactions.

Purpose of the Study:

  • To outline a methodology for using retrospective PT data to screen for matrix effects.
  • To identify factors influencing the interpretation of PT data, such as peer grouping and evaluation limits.
  • To demonstrate how to investigate bias-associated high unacceptability rates for potential matrix-induced interference.

Main Methods:

  • Utilized retrospective PT data from the College of American Pathologists (CAP) Survey Management Enhancement Program.

Related Experiment Videos

  • Analyzed data from the 1991 CAP Comprehensive Chemistry Survey.
  • Employed tabular and graphic representations of method performance to study bias and unacceptability rates.

    • Main Results:

    • Unacceptability rates in PT are often highest with significant methodologic bias and/or between-participant imprecision.
    • Factors like analyte concentration, peer group size, and evaluation limit type influence unacceptability rates.
    • Specific analytes (uric acid, glucose, creatinine, total protein) showed examples of significant bias potentially induced by matrix effects.

    Conclusions:

    • Retrospective PT data analysis is a viable strategy for detecting potential matrix effects.
    • Careful consideration of confounding factors is crucial before attributing high unacceptability rates solely to methodologic bias.
    • Further investigation is recommended when bias-associated high unacceptability rates are observed to confirm matrix effects and their causes.