Recommendations for Setting a Criterion and Assessing Commutability of Sample Materials Used in External Quality Assessment/Proficiency Testing Schemes.

It is important for external quality assessment materials (EQAMs) to be commutable with clinical samples; i.e., they should behave like clinical samples when measured using end-user clinical laboratory in vitro diagnostic medical devices (IVD-MDs). Using commutable EQAMs makes it possible to evaluate metrological traceability and/or equivalence of results between IVD-MDs. The criterion for assessing commutability of an EQAM between 2 IVD-MDs is that its result should be within the prediction interval limits based on the statistical distribution of the clinical sample results from the 2 IVD-MDs being compared. The width of the prediction interval is, among other things, dependent on the analytical performance characteristics of the IVD-MDs. A presupposition for using this criterion is that the differences in nonselectivity between the 2 IVD-MDs being compared are acceptable. An acceptable difference in nonselectivity should be small relative to the analytical performance specifications used in the external quality assessment scheme. The acceptable difference in nonselectivity is used to modify the prediction interval criterion for commutability assessment. The present report provides recommendations on how to establish a criterion for acceptable commutability for EQAMS, establish the difference in nonselectivity that can be accepted between IVD-MDs, and perform a commutability assessment. The report also contains examples for performing a commutability assessment of EQAMs.

Feasibility for aggregation of commutable external quality assessment results to evaluate metrological traceability and agreement among results.

Objectives: External quality assessment (EQA) with commutable samples is used for assessing agreement of results for patients' samples. We investigated the feasibility to aggregate results from four different EQA schemes to determine the bias between different measurement procedures and a reference target value. Methods: We aggregated EQA results for creatinine from programs that used commutable EQA material by calculating the relative difference between individual participant results and the reference target value for each sample. The means and standard errors of the means were calculated for the relative differences. Results were partitioned by methods, manufacturers and instrument platforms to evaluate the biases for the measurement procedures. Results: Data aggregated for enzymatic methods had biases that varied from -8.2 to 3.8% among seven instrument platforms for creatinine at normal concentrations (61-85 µmol/L). EQA schemes differed in the evidence provided about the commutability of their samples, and in the amount of detail collected from participants regarding the measurement procedures which limited the ability to sub-divide aggregated data by instrument platforms and models. Conclusions: EQA data could be aggregated from four different programs using different commutable samples to determine bias among different measurement procedures. Criteria for commutability for EQA samples as well as standardization of reporting the measurement methods, reagents, instrument platforms and models used by participants are needed to improve the ability to aggregate the results for optimal assessment of performance of measurement procedures. Aggregating data from a larger number of EQA schemes is feasible to assess trueness on a global scale.