Analysis of performance of clinical biochemistry laboratory using Sigma metrics and Quality Goal Index

Abstract

Background Unreliable and ingenuine results issued by clinical laboratories have serious consequences for the patients. Sigma metrics is a standardized tool for Quality assessment for test performance in a laboratory. Objective To evaluate the performance of routine biochemistry laboratory at MMIMSR, Mullana in terms of Sigma metrics and Quality Goal Index. Material and methods This cross sectional study evaluated performance of 14 routine chemistry parameters using retrospective Internal Quality Control data of two levels on Siemens Dimension Rxl from Feb to Jul 2019 for CV% and EQAS reports from CMC, Vellore for Bias%. Sigma metrics was calculated using total allowable error targets as per CLIA and Biological Variability database guidelines. Results For level-2 IQC; TG, Chol, ALP showed excellent performance with σ ​> ​6 while σ ​< ​3 was observed for AST, Total Protein, Glucose, BUN and ALT using CLIA guidelines while in IQC Level-3 poor performers were only BUN and ALT with Ca, TG and Chol showing σ ​> ​6. Further by using Biological Variability data guidelines; 10 parameters of IQC Level-2 and 5 of IQC level-3 were poor performers with σ ​< ​3. Conclusion Sigma metrics is an excellent tool for performance analysis of tests performed in a clinical laboratory. Lack of precision in terms of CV% was seen for majority of the poor performers. Total allowable error targets using Biological Variability data revealed σ ​< ​3 for 10 parameters while using CLIA guidelines σ ​< ​3 was seen for only 5 parameters of IQC level-2.

Evaluation of clinical biochemical laboratory analytical process performance using sigma metrics and quality goal index

“…The differences in sigma of the same analyte at different levels of QC material is ultimately due to makeable difference in CV% which is an indicator of poor precision mainly attributed to laboratory personnel and environment. Moreover, this finding is identical to other studies [10,17,18,21] . The discrepancy between the QC material levels can partly be attributed to methodology used for some analytes, which might indicate different performance between normal or abnormal concentrations of the QC materials [17] .…”

“…The current study went ahead to determine QGI for the analytes with σ 4 for purposes of identifying the problem cause. Majority of the poor to unacceptable analyte performance was due to imprecision (random errors), with only four analytes implicated to inaccuracy (systematic errors).Similar findings are in several previous studies implying most errors encountered in clinical laboratories are human and environmental errors [10,17,18,21] . In addition, σ values are influenced mainly by two inputs, bias and imprecision, which can be influenced by several factors within a clinical laboratory.…”

“…Following a detailed discussion by experts in this meeting, on understanding and setting up analytical performance goals in clinical laboratories, issued a supportive statement on the use of TEa values derived from CLIA guidelines [20] . On the other hand, some studies on six sigma performance analysis of clinical biochemistry laboratory have been reported based on TEa from biological variable data base (BVD) [10,15] . In support of using TEa from BVD, a Consensus Statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine, biological variation are model 2 for setting performance specifications [20] .…”

“…Six Sigma metric, is a generally-accepted and proven quality management tool that quantifies the performance of processes as a rate of Defects-Per-Million Opportunities, (DPM, or DPMO). Six Sigma also includes techniques like Define-Measure-Analyze Improve-Control (DMAIC), together with quality goal index, Root Cause analysis (RCA) can be performed to identify and eliminate defects and/or variations in a clinical biochemistry laboratory analytical process through measures of bias (EQAS) measure of inaccuracy, imprecision (CV% of IQC) and standard method specific total allowable error [8,10] . All laboratory errors in the analytical phase cannot be assessed, detected and eliminated by only running internal and external QCs hence the need for quantification at a scale of 0 to 6 by Sigma metrics [11] .…”

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk‐based statistical quality control strategy for these assays: A multicenter study

“…(3) monitor the calibration cycle of the detection system to reduce system errors; and (4) improve the maintenance of the instrument, replace old parts in a timely fashion, and improve the stability of the detection system. The research carried out by Goel et al 36 showed that QGI was very helpful for guiding a laboratory in determining the reasons for the poor performance of routine chemistry analyses and for guiding the adoption of appropriate improvement measures.…”