Deriving proper measurement uncertainty from Internal Quality Control data: An impossible mission?

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Metrology

Background:

• Measurement uncertainty (MU) quantifies the dispersion of results attributed to a measurand.
• The "top down" approach using Internal Quality Control (IQC) data is practical for clinical laboratories.
• Accurate MU calculation is crucial for reliable clinical decision-making.

Purpose of the Study:

• To explore diverse methods for calculating MU from IQC data in clinical settings.
• To highlight the challenges and considerations in estimating MU, particularly regarding bias.
• To discuss the implications of different approaches for laboratory practice.

Main Methods:

• Review of various formulas for MU calculation based on IQC data.
• Consideration of intermediate precision data as a minimal requirement.
• Inclusion of bias correction using certified reference materials, EQA schemes, or historical data.

Main Results:

• Multiple methods exist for calculating MU from IQC data, yielding different estimates.
• The "top down" approach using IQC is a convenient method for MU estimation.
• Bias estimation and correction significantly impact MU calculations.

Conclusions:

• No single, universally agreed-upon method for MU calculation in clinical laboratories currently exists.
• The choice of MU calculation method depends on available data and specific laboratory needs.
• Further standardization is needed for consistent MU reporting in clinical diagnostics.