Indian Journal of Medical Biochemistry

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VOLUME 26 , ISSUE 1 ( January-April, 2022 ) > List of Articles


Effect of Matrix and Source of Quality Specification Data on the Sigma Metrics of Common Chemistry Analytes in Clinical Laboratory

Srihita Mahavadi, Jayakumari Shanthakumari

Keywords : Biological variation, Clinical laboratory improvement amendments, Internal quality control, Matrix effect, The Sigma metric

Citation Information : Mahavadi S, Shanthakumari J. Effect of Matrix and Source of Quality Specification Data on the Sigma Metrics of Common Chemistry Analytes in Clinical Laboratory. Indian J Med Biochem 2022; 26 (1):1-8.

DOI: 10.5005/jp-journals-10054-0201

License: CC BY-NC 4.0

Published Online: 03-01-2023

Copyright Statement:  Copyright © 2022; The Author(s).


Introduction and aim: Internal and external quality control (IQC and EQC) is used to monitor and evaluate the analytical process. Six Sigma provides an objective assessment of performance. The Sigma metrics (σ) are calculated using the coefficient of variation (CV), bias, and total allowable error (TEa). One of the pitfalls of the Sigma metrics calculation is that it depends upon the source of the variables used in the formula and the measurand matrix. Hence, this study was conducted to calculate the Sigma metrics of urea, creatinine, Na, and K in serum and urine using Tea from biological variation (BV) (urine and serum) and Clinical Laboratory Improvement Amendments (CLIA) (serum) and subsequently comparing the Sigma metrics of all four analytes using TEa from BV between serum and urine control and using TEa from BV in the same matrix (serum). Materials and methods: A cross-sectional study was conducted in the Department of Clinical Biochemistry, St. John's Medical College for 1 year (January–December 2018). Bio-Rad IQC (serum and urine) data have been used to calculate σ of urea, creatinine, Na, and K. The cumulative CV and bias were obtained using unity real-time software from Bio-Rad Laboratories. Total allowable error values were obtained from BV and CLIA guidelines. Results: Urea, creatinine, Na, and K showed higher σ in the urine control than in serum controls indicating the better performance of these parameters in the urine matrix than in serum. In the same matrix (serum control), creatinine, Na, and K had higher σ using TEa from CLIA than TEa from BV. Na showed the highest difference in σ value between the two sources (p-value < 0.001). However, serum urea showed higher σ using TEa from BV compared to TEa from CLIA. Conclusion: Our study showed that σ varies with the matrix; henceforth, one should be careful in extrapolating the performance characteristics in terms of Sigma of an analyte from one matrix to another. In the same matrix, σ also varies depending on the source of TEa used in the calculation. It is, thus, essential to mention the source of the variables used to calculate σ for a better interpretation.

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