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Applying the Milan models to setting analytical performance specifications - considering all the information.

Graham R D Jones1,2, Katy J L Bell3, Ferruccio Ceriotti4

  • 1Department of Chemical Pathology, SydPath, St Vincent's Hospital, Darlinghurst, NSW, Australia.

Clinical Chemistry and Laboratory Medicine
|May 27, 2024
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Summary

Analytical performance specifications (APS) ensure pathology test quality. A risk-based approach, considering all Milan models and clinical impact, is proposed for setting optimal APS.

Keywords:
analytical performance specificationsbiological variationlaboratory qualitystate of the art

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Pathology

Background:

  • Analytical performance specifications (APS) are crucial for determining the necessary analytical quality of pathology tests to meet clinical demands.
  • The Milan models offer a framework for establishing APS based on clinical outcomes, biological variation, or the state-of-the-art.
  • Existing approaches assign measurands to models based on clinical use, physiological control, or lack of quality data.

Purpose of the Study:

  • To propose a refined risk-based approach for setting analytical performance specifications (APS).
  • To integrate information from all Milan models, considering the test's clinical pathway role and impact on decisions.
  • To determine the most appropriate APS for specific clinical settings.

Main Methods:

  • A risk-based framework is proposed, extending the Milan models.
  • This approach considers the test's purpose, role in the clinical pathway, and impact on medical decisions and outcomes.
  • It also incorporates biological variation, state-of-the-art, existing APS, and result usage in calculations.

Main Results:

  • The proposed method allows for a more comprehensive evaluation of APS requirements.
  • It emphasizes a holistic view, integrating multiple data sources beyond individual model assignments.
  • Consideration of existing APS and result utilization in calculations is highlighted as important.

Conclusions:

  • A risk-based approach, utilizing all Milan model information, provides a robust method for setting APS.
  • This comprehensive strategy ensures that APS align with the specific clinical context and impact of pathology tests.
  • The approach enhances the reliability and clinical utility of laboratory diagnostics.