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Review Papers | Biochemistry | India | Volume 12 Issue 3, March 2023
Use of Sigma Metrics in the Evaluation of Analytical Performance of Apo lipoproteins A1 and B Analytes
Dr Srilalitha | Dr Suresh [12] | Dr Ashwini Pandith
Abstract: Background: Standardization of Cardiovascular risk marker measurements is an essential prerequisite for improving cardiac health. Six Sigma metrics, is used to assess the analytical quality of automated clinical chemistry parameters in clinical laboratory and to explore the importance of the source used for estimation of the allowable total error. Analytical performance and quality specifications were evaluated for the parameters of Apo A and Apo B parameters in cardiovascular risk patients to assess the risk in this study. Method: Coefficient of Variation (CV%) and External Quality Assurance Scheme (EQAS) bias% data for Apolipoprotein A and Apolipoprotein B analytes were collected for the year 2022 for a period of 6 months. TEa calculated for each analyte was calculated based on average CV% and bias%. Total TEa calculated values are compared with optimal, minimal and desirable TEa of each analyte. Six Sigma Score is evaluated for Apo A & Apo B parameters against Minimum, Desirable & Optimum quality specifications. Results: Average CV % is within acceptable limits as per Desirable & Optimum specifications whereas not acceptable as per Minimum specifications. Percentage bias is within acceptable limits as per all 3 specifications. TEa is within acceptable limits as per Desirable & Optimum specifications whereas not acceptable as per Minimum specifications. Six Sigma score was acceptable for Apo A under only Optimum quality specification. For Apo B, Six Sigma score was acceptable under both desirable & optimum quality specifications. Conclusion: Sigma metrics is an excellent quality management tool and quantitatively to evaluate analytical performance. The accurate results generated are useful for clinicians for decision making.
Keywords: Apo lipoprotein A1, Apolipiprotein B, Coefficient variation (CV), external quality assurance scheme, EQAS, Total allowable error, TEa
Edition: Volume 12 Issue 3, March 2023,
Pages: 1053 - 1057