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Is statistical evaluation sufficient? New external quality assessment performance metrics through clinical
Halil İbrahim Akbay1, Elvar Theodorsson2, Hamit Hakan Alp1
1Department of Medical Biochemistry, Faculty of Medicine, Van Yüzüncü Yıl University, Van, Türkiye.
Clinical Chemistry and Laboratory Medicine
|June 18, 2026
Summary
New metrics for External Quality Assessment (EQA) integrate biological variation, improving clinical relevance. These tools, SDI_RCV and pSDI, enhance laboratory quality assessment by detecting biases missed by conventional methods.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Quality Management Systems
Background:
- External Quality Assessment (EQA) traditionally uses Standard Deviation Index (SDI) with fixed limits (-2 to +2).
- This conventional SDI does not account for biological variation or clinical relevance of laboratory results.
- Analytical imprecision is a key concern in diagnostic testing.
Purpose of the Study:
- To develop and validate novel metrics (SDI_RCV and pSDI) that incorporate biological variation into EQA interpretation.
- To quantify sources of analytical imprecision using variance component analysis (VCA).
- To establish a biologically grounded framework for laboratory quality assessment.
Main Methods:
- Analysis of monthly EQA results for 45 analytes over 12 cycles in a clinical laboratory.
- Calculation of SDI_RCV (ratio of reference change value to peer-group CV) and pSDI (EQA bias as a percentage of RCV).
- Decomposition of total imprecision into repeatability, between-month variation, and reagent lot-to-lot variation using VCA.
Main Results:
- Nine observations (1.67%) showed clinically significant bias despite acceptable SDI (-2
- Four observations (0.74%) had clinically inconsequential SDI exceedance (SDI≥±3 yet -100%
- Lot-to-lot variation contributed a median of 23.6% to total variance, exceeding 50% for six analytes.
- Four observations (0.74%) had clinically inconsequential SDI exceedance (SDI≥±3 yet -100%
Conclusions:
- SDI_RCV and pSDI effectively detect false acceptance and false rejection in conventional EQA criteria.
- These novel metrics provide a biologically grounded approach for aligning laboratory quality assessment with clinical needs.
- Variance component analysis highlights the impact of reagent lot variation on analytical imprecision.
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