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The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?

INTRODUCTION: By quantifying the measurement uncertainty (MU), both the laboratory and the physician can have an objective estimate of the results’ quality. There is significant flexibility on how to determine the MU in laboratory medicine and different approaches have been proposed by Nordtest, Eur...

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Autores principales: Martinello, Flávia, Snoj, Nada, Skitek, Milan, Jerin, Aleš
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Croatian Society of Medical Biochemistry and Laboratory Medicine 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138004/
https://www.ncbi.nlm.nih.gov/pubmed/32292278
http://dx.doi.org/10.11613/BM.2020.020101
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author Martinello, Flávia
Snoj, Nada
Skitek, Milan
Jerin, Aleš
author_facet Martinello, Flávia
Snoj, Nada
Skitek, Milan
Jerin, Aleš
author_sort Martinello, Flávia
collection PubMed
description INTRODUCTION: By quantifying the measurement uncertainty (MU), both the laboratory and the physician can have an objective estimate of the results’ quality. There is significant flexibility on how to determine the MU in laboratory medicine and different approaches have been proposed by Nordtest, Eurolab and Cofrac to obtain the data and apply them in formulas. The purpose of this study is to compare three different top-down approaches for the estimation of the MU and to suggest which of these approaches could be the most suitable choice for routine use in clinical laboratories. MATERIALS AND METHODS: Imprecision and bias of the methods were considered as components of the MU. The bias was obtained from certified reference calibrators (CRC), proficiency tests (PT), and inter-laboratory internal quality control scheme (IQCS) programs. The bias uncertainty, the combined and the expanded uncertainty were estimated using the Nordtest, Eurolab and Cofrac approaches. RESULTS: Using different approaches, the expanded uncertainty estimates ranged from 18.9-40.4%, 18.2-22.8%, 9.3-20.9%, and 7.1-18.6% for cancer antigen (CA) 19-9, testosterone, alkaline phosphatase (ALP), and creatinine, respectively. Permissible values for MU and total error ranged from 16.0-46.1%, 13.1-21.6%, 10.7-26.2%, and 7.5-17.3%, respectively. CONCLUSION: The bias was highest using PT, followed by CRC and IQCS data, which were similar. The Cofrac approach showed the highest uncertainties, followed by Eurolab and Nordtest. However, the Eurolab approach requires additional measurements to obtain uncertainty data. In summary, the Nordtest approach using IQCS data was therefore found to be the most practical formula.
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spelling pubmed-71380042020-04-15 The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine? Martinello, Flávia Snoj, Nada Skitek, Milan Jerin, Aleš Biochem Med (Zagreb) Lessons in Biostatistics INTRODUCTION: By quantifying the measurement uncertainty (MU), both the laboratory and the physician can have an objective estimate of the results’ quality. There is significant flexibility on how to determine the MU in laboratory medicine and different approaches have been proposed by Nordtest, Eurolab and Cofrac to obtain the data and apply them in formulas. The purpose of this study is to compare three different top-down approaches for the estimation of the MU and to suggest which of these approaches could be the most suitable choice for routine use in clinical laboratories. MATERIALS AND METHODS: Imprecision and bias of the methods were considered as components of the MU. The bias was obtained from certified reference calibrators (CRC), proficiency tests (PT), and inter-laboratory internal quality control scheme (IQCS) programs. The bias uncertainty, the combined and the expanded uncertainty were estimated using the Nordtest, Eurolab and Cofrac approaches. RESULTS: Using different approaches, the expanded uncertainty estimates ranged from 18.9-40.4%, 18.2-22.8%, 9.3-20.9%, and 7.1-18.6% for cancer antigen (CA) 19-9, testosterone, alkaline phosphatase (ALP), and creatinine, respectively. Permissible values for MU and total error ranged from 16.0-46.1%, 13.1-21.6%, 10.7-26.2%, and 7.5-17.3%, respectively. CONCLUSION: The bias was highest using PT, followed by CRC and IQCS data, which were similar. The Cofrac approach showed the highest uncertainties, followed by Eurolab and Nordtest. However, the Eurolab approach requires additional measurements to obtain uncertainty data. In summary, the Nordtest approach using IQCS data was therefore found to be the most practical formula. Croatian Society of Medical Biochemistry and Laboratory Medicine 2020-04-15 2020-06-15 /pmc/articles/PMC7138004/ /pubmed/32292278 http://dx.doi.org/10.11613/BM.2020.020101 Text en Croatian Society of Medical Biochemistry and Laboratory Medicine. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Lessons in Biostatistics
Martinello, Flávia
Snoj, Nada
Skitek, Milan
Jerin, Aleš
The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title_full The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title_fullStr The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title_full_unstemmed The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title_short The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
title_sort top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?
topic Lessons in Biostatistics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138004/
https://www.ncbi.nlm.nih.gov/pubmed/32292278
http://dx.doi.org/10.11613/BM.2020.020101
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