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| Year:
2023 |
Month:
April
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Volume:
12 |
Issue:
2 |
Page:
PO14 - PO18 |
Sigma Metrics as a Valuable Tool for Effective Analytical Performance and Quality Control Planning in Clinical Laboratory: A Retrospective Study
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Correspondence
Anupapama Raj Karaattuthazhathu, PP Sathi, Lathi Nair, PM Ramees, Reenu Manayani, Elizabeth Benny, Mary Benny,
Dr. Anupama Raj Karaattuthazhathu,
Associate Professor, KMCT Medical College Hospital, P.O. Manassery, Kozhikode, Kerala, India.
E-mail: anupamarojith@gmail.com :
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Introduction: For the release of precise and accurate reports of routine tests, its necessary to follow a proper quality management system in the clinical laboratory. One of the most popular quality management system tools, for process improvement, six sigma has been accepted widely in the laboratory testing process. It gives an objective assessment of analytical methods and instrumentation. Six sigma measures the outcome of a process, on a scale of 0 to 6. The poor outcomes are measured in terms of defects per million opportunities (DPMO).
Aim: To do the performance assessment of each analyte by six sigma analysis and to plan and chart out a better, customised, quality control plan for each analyte, according to its own sigma value.
Materials and Methods: This was a retrospective observational study, conducted from January 2022 to June 2022, in the Department of Central Laboratory, KMCT Medical College, Kozhikode, Kerala, India. The precision and accuracy of 26 parameters in both haematology and biochemistry were assessed via Internal Quality Control (IQC) and External Quality Assurance (EQAS) Programme, analysis, and their performance was assessed by sigma analysis.
Results: Clinical chemistry parameters showed an average percentage of Coefficient of Variation (CV%) of 2.65% and 2.3% for all the parameters in L2 (normal level) and L3 (abnormal levels) respectively. In haematology, the average CV% came out as, 1.3% (high level),1.82% (low level), and 1.35% (normal level). These values indicate excellent precision for all parameters in both clinical chemistry and haematology; with CV% below 3%. It was observed in the month of May, due to reconstitution errors, bias% showed a setback in a few chemistry parameters, due to which the sigma was lowered. Parameters with <3 sigma metrics (poor performance) occupy 37%, 3-6 sigma metrics (good performance) occupy 29% and >6 sigma metrics (world-class performance) occupy 34% of all the 26 parameters of clinical chemistry and haematology. Mean, Standard Deviation (SD) for biochemistry parameters were calculated using the daily IQC data.
Conclusion: With the present study, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of the existing laboratory processes. It is on the basis of strict quality control measures and sigma analysis, the present Institute, was able to achieve world-class performance in many analytes of clinical chemistry and haematology disciplines. However, a few analytes like alkaline phosphatase, alanine transaminase, aspartate transaminase, and total protein needed more stringent external quality assurance monitoring and modified quality control measures.
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