The establishment of an accurate and dependable quality control (QC) system is essential for clinical laboratories to guarantee the precision of patient test outcomes. This study sought to enhance internal quality control (IQC) protocols for ten clinical chemistry parameters at the Al Ihsan Regional Hospital Laboratory through the application of Sigma metrics. A cross-sectional investigation utilized six months of Internal Quality Control data, including AST, ALT, Ureum, creatinine, glucose, uric acid, cholesterol, triglycerides, HDL, and LDL. The performance of each analyte was assessed by computing bias, precision (CV), and sigma values utilizing four distinct Total Allowable Error (TEa) sources (CLIA, RCPA, RiliBAK, and Biological Variation) in accordance with the 2014 Milan consensus hierarchy. The results exhibited a broad spectrum of analytical performance across the evaluated parameters. Nine of the ten analytes showed compatibility with various TEa sources, but Ureum exhibited satisfactory performance just with TEa derived from biological variation. Performance levels showed considerable variation, with analytes like creatinine attaining exceptional performance (>6 sigma), while Ureum demonstrated poor performance (<3 sigma). The study concludes that the Sigma measures offer a comprehensive, quantitative foundation for developing a risk-based IQC approach. By customizing Westgard rules to the sigma performance of each analyte, laboratories may guarantee test quality while markedly enhancing efficiency and minimizing superfluous QC cycles. This data-centric methodology facilitates the optimization of laboratory resources in accordance with ISO 15189 standards.
Furqon et al. (Wed,) studied this question.