Quantitative Application of Sigma Metrics in Medical Biochemistry
Introduction: Laboratory errors are result of a poorly designed quality system in the laboratory. Six Sigma is an error reduction methodology that has been successfully applied at Motorola and General Electric. Sigma (σ) is the mathematical symbol for standard deviation (SD). Sigma methodology c...
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doaj-66994150a3bb4c6b85db9ece1119352f2020-11-25T01:59:39ZengJCDR Research and Publications Private LimitedJournal of Clinical and Diagnostic Research2249-782X0973-709X2013-12-017122689269110.7860/JCDR/2013/7292.3700Quantitative Application of Sigma Metrics in Medical BiochemistrySunil Kumar Nanda0Lopamudra Ray1Associate Professor, Department of Biochemistry, Pondicherry Institute of Medical Sciences, Ganapathichettikulam Kalapet, Pondicherry – 605014, India.Assistant Professor, Pondicherry Institute of Medical Sciences, Ganapathichettikulam Kalapet, Pondicherry – 605014, India.Introduction: Laboratory errors are result of a poorly designed quality system in the laboratory. Six Sigma is an error reduction methodology that has been successfully applied at Motorola and General Electric. Sigma (σ) is the mathematical symbol for standard deviation (SD). Sigma methodology can be applied wherever an outcome of a process has to be measured. A poor outcome is counted as an error or defect. This is quantified as defects per million (DPM). A six sigma process is one in which 99.999666% of the products manufactured are statistically expected to be free of defects. Six sigma concentrates, on regulating a process to 6 SDs, represents 3.4 DPM (defects per million) opportunities. It can be inferred that as sigma increases, the consistency and steadiness of the test improves, thereby reducing the operating costs. We aimed to gauge performance of our laboratory parameters by sigma metrics. Objectives: Evaluation of sigma metrics in interpretation of parameter performance in clinical biochemistry. Material and Methods: The six month internal QC (October 2012 to march 2013) and EQAS (external quality assurance scheme) were extracted for the parameters-Glucose, Urea, Creatinine, Total Bilirubin, Total Protein, Albumin, Uric acid, Total Cholesterol, Triglycerides, Chloride, SGOT, SGPT and ALP. Coefficient of variance (CV) were calculated from internal QC for these parameters. Percentage bias for these parameters was calculated from the EQAS. Total allowable errors were followed as per Clinical Laboratory Improvement Amendments (CLIA) guidelines. Sigma metrics were calculated from CV, percentage bias and total allowable error for the above mentioned parameters. Results: For parameters - Total bilirubin, uric acid, SGOT, SGPT and ALP, the sigma values were found to be more than 6. For parameters – glucose, Creatinine, triglycerides, urea, the sigma values were found to be between 3 to 6. For parameters – total protein, albumin, cholesterol and chloride, the sigma values were found to be less than 3. Conclusion: ALP was the best performer when it was gauzed on the sigma scale, with a sigma metrics value of 8.4 and chloride had the least sigma metrics value of 1.4.https://jcdr.net/articles/PDF/3700/11-%207292_E(C)_F(H)_PF1(PP)_PFA(H)_OLF.pdfcoefficient of variancepercentage biastotal allowable error |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sunil Kumar Nanda Lopamudra Ray |
spellingShingle |
Sunil Kumar Nanda Lopamudra Ray Quantitative Application of Sigma Metrics in Medical Biochemistry Journal of Clinical and Diagnostic Research coefficient of variance percentage bias total allowable error |
author_facet |
Sunil Kumar Nanda Lopamudra Ray |
author_sort |
Sunil Kumar Nanda |
title |
Quantitative Application of Sigma Metrics in Medical Biochemistry |
title_short |
Quantitative Application of Sigma Metrics in Medical Biochemistry |
title_full |
Quantitative Application of Sigma Metrics in Medical Biochemistry |
title_fullStr |
Quantitative Application of Sigma Metrics in Medical Biochemistry |
title_full_unstemmed |
Quantitative Application of Sigma Metrics in Medical Biochemistry |
title_sort |
quantitative application of sigma metrics in medical biochemistry |
publisher |
JCDR Research and Publications Private Limited |
series |
Journal of Clinical and Diagnostic Research |
issn |
2249-782X 0973-709X |
publishDate |
2013-12-01 |
description |
Introduction: Laboratory errors are result of a poorly designed
quality system in the laboratory. Six Sigma is an error reduction
methodology that has been successfully applied at Motorola
and General Electric. Sigma (σ) is the mathematical symbol for
standard deviation (SD). Sigma methodology can be applied
wherever an outcome of a process has to be measured. A poor
outcome is counted as an error or defect. This is quantified as
defects per million (DPM). A six sigma process is one in which
99.999666% of the products manufactured are statistically
expected to be free of defects. Six sigma concentrates, on
regulating a process to 6 SDs, represents 3.4 DPM (defects per
million) opportunities. It can be inferred that as sigma increases,
the consistency and steadiness of the test improves, thereby
reducing the operating costs. We aimed to gauge performance
of our laboratory parameters by sigma metrics.
Objectives: Evaluation of sigma metrics in interpretation of
parameter performance in clinical biochemistry.
Material and Methods: The six month internal QC (October
2012 to march 2013) and EQAS (external quality assurance
scheme) were extracted for the parameters-Glucose, Urea,
Creatinine, Total Bilirubin, Total Protein, Albumin, Uric acid,
Total Cholesterol, Triglycerides, Chloride, SGOT, SGPT and ALP.
Coefficient of variance (CV) were calculated from internal QC for
these parameters. Percentage bias for these parameters was
calculated from the EQAS. Total allowable errors were followed
as per Clinical Laboratory Improvement Amendments (CLIA)
guidelines. Sigma metrics were calculated from CV, percentage
bias and total allowable error for the above mentioned
parameters.
Results: For parameters - Total bilirubin, uric acid, SGOT, SGPT
and ALP, the sigma values were found to be more than 6. For
parameters – glucose, Creatinine, triglycerides, urea, the sigma
values were found to be between 3 to 6. For parameters – total
protein, albumin, cholesterol and chloride, the sigma values
were found to be less than 3.
Conclusion: ALP was the best performer when it was gauzed on
the sigma scale, with a sigma metrics value of 8.4 and chloride
had the least sigma metrics value of 1.4. |
topic |
coefficient of variance percentage bias total allowable error |
url |
https://jcdr.net/articles/PDF/3700/11-%207292_E(C)_F(H)_PF1(PP)_PFA(H)_OLF.pdf |
work_keys_str_mv |
AT sunilkumarnanda quantitativeapplicationofsigmametricsinmedicalbiochemistry AT lopamudraray quantitativeapplicationofsigmametricsinmedicalbiochemistry |
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