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Precision quality control: a dynamic model for risk-based analysis of analytical quality.
Robert L Schmidt1,2, Ryleigh A Moore3, Brandon S Walker2
1Department of Pathology, University of Utah, Salt Lake City, UT, USA.
Clinical Chemistry and Laboratory Medicine
|January 9, 2023
Summary
Improving clinical laboratory quality control (QC) requires better risk-based methods. Dynamic risk models and trade-off curves offer advantages over static models for analyzing QC performance and optimizing cost-effectiveness.
Area of Science:
- Clinical laboratory science
- Medical diagnostics
- Biostatistics
Background:
- Clinical laboratory testing faces ongoing pressure to enhance cost-effectiveness.
- Risk-based approaches are promising for quality control (QC) but have shown limitations in current methods.
- There is a clear need for enhanced risk-based QC methodologies.
Purpose of the Study:
- To present a dynamic model for assay behavior analysis.
- To evaluate the practical application of this model through simulation.
- To compare the performance of traditional Shewhart QC monitoring with Westgard rules.
Main Methods:
- Development and application of a dynamic model for assay behavior.
- Simulation studies to assess model performance.
- Comparison of Shewhart control charts and Westgard rules.
- Utilizing trade-off curves for QC performance analysis.
Main Results:
- Westgard rules demonstrated superior performance compared to simple Shewhart control within specific ranges of the false-positive and false-negative risk trade-off.
- Risk trade-off analysis can be visualized using risk, cost, or risk-versus-cost metrics.
- Log transformation can be applied to "smooth" risk trade-off curves for clearer interpretation.
Conclusions:
- Dynamic risk models offer potential benefits over static models for risk-based QC analysis in clinical laboratories.
- The findings support the refinement of QC strategies using advanced modeling techniques.
- Improved QC methods can lead to more cost-effective and reliable clinical testing.
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