Comparison of Sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes.

Objectives: Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation. Methods: Sigma metrics were computed for 33 chemistry and 26 immunoassay analytes on the Roche Cobas 6000 analyzer. Bias was estimated by three approaches: (1) averaging the monthly bias values obtained from the external quality assurance (EQA) studies; (2) calculating the bias values from the regression equation derived from the EQA data; and (3) averaging the monthly bias values from the internal quality control (IQC) events. Sigma metrics were separately calculated for the two levels of the IQC samples using three bias estimation approaches. The resulting Sigma values were classified into five categories considering Westgard Sigma Rules as ≥6, <6 and ≥5, <5 and ≥4, <4 and ≥3, and <3. Results: When classifying Sigma metrics estimated by three bias estimation approaches for each assay, 16 chemistry assays at the IQC level 1 and 2 were observed to fall into different Sigma categories under at least one bias estimation approach. Similarly, for 12 immunoassays at the IQC level 1 and 2, Sigma category was different depending on bias estimation approach. Conclusions: Sigma metrics may differ depending on bias estimation approaches. This should be considered when using Six Sigma for assessing analytical performance or scheduling the IQC events.