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

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.