Background: The clinical laboratory in today’s world is a rapidly evolving field which faces a constant pressure to produce quick and reliable results. Sigma metric is a new tool which helps to reduce process variability, quantitate the approximate number of analytical errors, and evaluate and guide for better quality control (QC) practices.
Objectives: To analyze sigma metrics of 16 biochemistry analytes using ERBA XL 200 Biochemistry analyzer, interpret parameter performance, compare analyzer performance with other Middle East studies and modify existing QC practices.
Materials and Methods: This study was undertaken at a clinical laboratory for a period of 12 months from January to December 2020 for the following analytes: albumin (ALB), alanine amino transferase (SGPT), aspartate amino transferase (SGOT), alkaline phosphatase (ALKP), bilirubin total (BIL T), bilirubin direct (BIL D), calcium (CAL), cholesterol (CHOL), creatinine (CREAT), gamma glutamyl transferase (GGT), glucose (GLUC), high density lipoprotein (HDL), triglyceride (TG), total protein (PROT), uric acid (UA) and urea. The Coefficient of variance (CV%) and Bias % were calculated from internal quality control (IQC) and external quality assurance scheme (EQAS) records respectively. Total allowable error (TEa) was obtained using guidelines Clinical Laboratories Improvement Act guidelines (CLIA). Sigma metrics was calculated using CV%, Bias% and TEa for the above parameters.
Results: It was found that 5 analytes in level 1 and 8 analytes in level 2 had greater than 6 sigma performance indicating world class quality. Cholesterol, glucose (level 1 and 2) and creatinine level 1 showed >4 sigma performance i.e acceptable performance. Urea (both levels) and GGT (level 1) showed Conclusion: Sigma metrics helps to assess analytic methodologies and can serve as an important self assessment tool for quality assurance in the clinical laboratory. Sigma metric evaluation in this study helped to evaluate the quality of several analytes and also categorize them from high performing to problematic analytes, indicating the utility of this tool. In conclusion, parameters showing lesser than 3 sigma need strict monitoring and modification of quality control procedure with change in method if necessary.
Six sigma, Westgard rule, Bias, Total allowable error, Laboratory quality control.