Sigma Metrics: A Powerful Tool for Performance Evaluation and Quality Control Planning in a Clinical Biochemistry Laboratory

Abstract

Introduction: Any error in the laboratory testing processes can affect the diagnosis and patient management. Six Sigma is a data driven quality management system for identifying and reducing errors and variations in clinical laboratory processes. Aim: This study was carried out to estimate Sigma metrics of various biochemical analytes in order to evaluate performance of quality control and implement optimum quality control strategy for each analyte. Quality goal index (QGI) was also calculated to identify the problems of inaccuracy and imprecision for parameters having lower sigma values. Materials and Methods: This retrospective, observational study was conducted at the Central Clinical Biochemistry Laboratory of Seth GS Medical College and KEM hospital in Mumbai for a period of six months (July 2019 to December 2019). Sigma metrics for 20 analytes was calculated by using internal quality control and external quality control data. Further, QGI was calculated for analytes having sigma value of <4 to identify imprecision or inaccuracy. Statistical analysis was performed using Microsoft office excel 2010 software. Results: Total protein, Glucose, Urea, Triglyceride (TAG), High Density Lipoprotein (HDL), and Low Density Lipoprotein (LDL) for normal (L1) and pathological (L2) controls achieved excellent performance (>6 sigma). Westgard rule (13s) with two control measurement (N2) per QC event and run size (R1000) i.e. 1000 patient samples between consecutive QC events was adopted for these analytes. For analytes with sigma value of 4-6, more rules (sigma 4-5: Westgardrules-13s/22s/R4s/41s, N4 and R200 and for sigma value 5-6: 13S/22s/R4s, N2 and R450) were adopted. The sigma values of six analytes (Creatinine, Sodium, Potassium, Calcium, Chloride, Inorganic phosphate) were <4 at one or more QC levels. For these analytes, strict QC procedures (Westgard rules-13s/22s/R4s/41s/6x, N4 and R45) were incorporated. QGI of these analytes was <0.8 which indicated the problem of imprecision. Staff training programs and review of standard operating procedures were done for these analytes to improve method performance. Conclusion: Sigma Metrics estimation helps in designing optimum QC protocols, minimising unnecessary QC runs and reducing the cost for analytes having high sigma metrics. Focused and effective QC strategy for analytes having low sigma helps in improving the performance of those analytes.