Assessment of Analytical Quality through Sigma Metrics & its Application for Selection of Westgard Rule

In today's world, the clinical laboratory is a rapidly expanding area under constant demand to give speedy and reliable results. A clinical laboratory's performance is measured using IQC and EQAS. However, these approaches cannot quantify the number of errors. A new tool called sigma metric quantifies the approximate amount of analytical errors, and assesses and directs the development of better quality control procedures. In order to minimizing error rates Six Sigma were utilized to quantify the analytical quality of automated clinical chemistry. Objective: This study was conducted to estimate Sigma metrics and Quality Goal Index of various biochemical analytes in order to evaluate quality control performance and execute the best quality control approach for each analyte. Material and method: IQC and EQA data were examined using a chemistry auto analyzer (Architect C 8000) at the Biochemistry laboratory, Sir T Hospital, Bhavnagar, from January 2022 to October 2022. Mean, standard deviation, coefficient of variation %, bias % and sigma metrics and Quality Goal Index were calculated for Plasma Glucose, serum Urea, Creatinine, alanine ALT, AST, Total protein, Albumin, ALP, Total Cholesterol, Triglyceride, HDL, LDL, Uric acid and LDH. Results: Excellent sigma values were elicited for SGOT (Level 2), ALP, Triglycerides, HDL, and Uric acid. Satisfactory sigma values were elicited for, Creatinine (both the levels) TP, LDH (Level 1), SGPT, LDL (Level 2), while Glucose, Albumin, Cholesterol, (both the levels) SGPT, LDL (Level 1), TP (Level 2) having sigma value <3. Conclusion: Sigma metrics is useful for addressing poor performance in assessments, improves laboratory performance and aids in the evaluation of analytical techniques. It serves as a roadmap for developing a quality control strategy. It can be used as a self-evaluation tool for clinical laboratories