Background: Six Sigma metrics were used to assess the analytical quality of automated clinical chemistry and immunoassay tests in a large Belgian clinical laboratory and to explore the importance of the source used for estimation of the allowable total error. Clinical laboratories are continually challenged to maintain analytical quality. However, it is difficult to measure assay quality objectively and quantitatively.
Methods: The Sigma metric is a single number that estimates quality based on the traditional parameters used in the clinical laboratory: allowable total error (TEa), precision and bias. In this study, Sigma metrics were calculated for 41 clinical chemistry assays for serum and urine on five ARCHITECT c16000 chemistry analyzers. Controls at two analyte concentrations were tested and Sigma metrics were calculated using three different TEa targets (Ricos biological variability, CLIA, and RiliBÄK).
Results: Sigma metrics varied with analyte concentration, the TEa target, and between/among analyzers. Sigma values identified those assays that are analytically robust and require minimal quality control rules and those that exhibit more variability and require more complex rules. The analyzer to analyzer variability was assessed on the basis of Sigma metrics.
Conclusions: Six Sigma is a more efficient way to control quality, but the lack of TEa targets for many analytes and the sometimes inconsistent TEa targets from different sources are important variables for the interpretation and the application of Sigma metrics in a routine clinical laboratory. Sigma metrics are a valuable means of comparing the analytical quality of two or more analyzers to ensure the comparability of patient test results.
The technical support by Ilse Van Gysel and Patrick Rosiers was much appreciated. We also thank Frank Heyvaert for his professional cooperation.
Conflict of interest statement
Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article. Employment and fees for lecturing played no role in thestudy design; in the collection, analysis, and interpretationof data; in the writing of the report; or in the decision tosubmit the report for publication.
Research funding: None declared.
Employment or leadership: Mario Berth: has received fees from Abbott for lecturing. Dave Armbruster: is employed by Abbott Diagnostics; receives salary from and holds stock from Abbott. Sten Westgard: has received fees from Abbott for lecturing and preparing educational materials.
Honorarium: None declared.
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