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...
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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. |
doi_str_mv | 10.11613/BM.2020.020101 |
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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.</description><identifier>ISSN: 1330-0962</identifier><identifier>ISSN: 1846-7482</identifier><identifier>EISSN: 1846-7482</identifier><identifier>DOI: 10.11613/BM.2020.020101</identifier><identifier>PMID: 32292278</identifier><language>eng</language><publisher>ZAGREB: Croatian Soc Medical Biochemistry & Laboratory Medicine</publisher><subject>Alkaline Phosphatase - analysis ; Alkaline Phosphatase - metabolism ; Antigens, Neoplasm - analysis ; bias ; Clinical Laboratory Techniques - methods ; Clinical Laboratory Techniques - standards ; Creatinine - analysis ; Diagnostic Errors ; Humans ; Lessons in Biostatistics ; Life Sciences & Biomedicine ; measurement uncertainty ; Medical Laboratory Technology ; Quality Control ; Science & Technology ; Testosterone - analysis ; total error ; Uncertainty</subject><ispartof>Biochemia Medica, 2020-06, Vol.30 (2), p.020101-195, Article 020101</ispartof><rights>Croatian Society of Medical Biochemistry and Laboratory Medicine.</rights><rights>Croatian Society of Medical Biochemistry and Laboratory Medicine. 2020 Croatian Society of Medical Biochemistry</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>18</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000540760400001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c390t-fc45851d16397c7cac79ae4b46844ebdb98dea64d8d668808b8a48730a1440583</citedby><cites>FETCH-LOGICAL-c390t-fc45851d16397c7cac79ae4b46844ebdb98dea64d8d668808b8a48730a1440583</cites><orcidid>0000-0002-6073-3404</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138004/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138004/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2115,27929,27930,28253,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32292278$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Martinello, Flavia</creatorcontrib><creatorcontrib>Snoj, Nada</creatorcontrib><creatorcontrib>Skitek, Milan</creatorcontrib><creatorcontrib>Jerin, Ales</creatorcontrib><title>The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?</title><title>Biochemia Medica</title><addtitle>BIOCHEM MEDICA</addtitle><addtitle>Biochem Med (Zagreb)</addtitle><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.</description><subject>Alkaline Phosphatase - analysis</subject><subject>Alkaline Phosphatase - metabolism</subject><subject>Antigens, Neoplasm - analysis</subject><subject>bias</subject><subject>Clinical Laboratory Techniques - methods</subject><subject>Clinical Laboratory Techniques - standards</subject><subject>Creatinine - analysis</subject><subject>Diagnostic Errors</subject><subject>Humans</subject><subject>Lessons in Biostatistics</subject><subject>Life Sciences & Biomedicine</subject><subject>measurement uncertainty</subject><subject>Medical Laboratory Technology</subject><subject>Quality Control</subject><subject>Science & Technology</subject><subject>Testosterone - analysis</subject><subject>total error</subject><subject>Uncertainty</subject><issn>1330-0962</issn><issn>1846-7482</issn><issn>1846-7482</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>EIF</sourceid><recordid>eNqNkc1v1DAQxSMEoqVw5oZ8RELZjj-SOBxAdMWX1IpLOVuOMyGGxF5sh9X-95hNWdEbB8tP49-8GesVxXMKG0pryi-vbjYMGGzyoUAfFOdUirpshGQPs-YcSmhrdlY8ifE7QFVVsnlcnHHGWsYaeV7g7Ygk-V3Z-70jercLXpsxV8iMOi4BZ3SJLM5gSNq6dHhN9qPNxODDvEyaxNEvU0_2SJaIxDoy6c4HnXw4ZIveGuvw7dPi0aCniM_u7ovi64f3t9tP5fWXj5-3765Lw1tI5WBEJSva05q3jWmMNk2rUXSilkJg13et7FHXopd9XUsJspNayIaDpkJAJflF8Wb13S1dHm7y7kFPahfsrMNBeW3V_RdnR_XN_1IN5RJAZINyNRiD0T_uNa6VGAxmqRiXLUDmX94NDP7ngjGp2UaD06Qd-iVmrAVaMXnc7XJFTfAxBhxO7hTUMUx1daP-hKnWMHPHi39_c-L_ppeBVyuwx84P0VjMOZ0wyHkLaGoQWR3t5P_TW5t0st5t_eIS_w2q87zt</recordid><startdate>20200615</startdate><enddate>20200615</enddate><creator>Martinello, Flavia</creator><creator>Snoj, Nada</creator><creator>Skitek, Milan</creator><creator>Jerin, Ales</creator><general>Croatian Soc Medical Biochemistry & Laboratory Medicine</general><general>Medicinska naklada</general><general>Croatian Society of Medical Biochemistry and Laboratory Medicine</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>VP8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6073-3404</orcidid></search><sort><creationdate>20200615</creationdate><title>The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?</title><author>Martinello, Flavia ; Snoj, Nada ; Skitek, Milan ; Jerin, Ales</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-fc45851d16397c7cac79ae4b46844ebdb98dea64d8d668808b8a48730a1440583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Alkaline Phosphatase - analysis</topic><topic>Alkaline Phosphatase - metabolism</topic><topic>Antigens, Neoplasm - analysis</topic><topic>bias</topic><topic>Clinical Laboratory Techniques - methods</topic><topic>Clinical Laboratory Techniques - standards</topic><topic>Creatinine - analysis</topic><topic>Diagnostic Errors</topic><topic>Humans</topic><topic>Lessons in Biostatistics</topic><topic>Life Sciences & Biomedicine</topic><topic>measurement uncertainty</topic><topic>Medical Laboratory Technology</topic><topic>Quality Control</topic><topic>Science & Technology</topic><topic>Testosterone - analysis</topic><topic>total error</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Martinello, Flavia</creatorcontrib><creatorcontrib>Snoj, Nada</creatorcontrib><creatorcontrib>Skitek, Milan</creatorcontrib><creatorcontrib>Jerin, Ales</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hrcak: Portal of scientific journals of Croatia</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Biochemia Medica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Martinello, Flavia</au><au>Snoj, Nada</au><au>Skitek, Milan</au><au>Jerin, Ales</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine?</atitle><jtitle>Biochemia Medica</jtitle><stitle>BIOCHEM MEDICA</stitle><addtitle>Biochem Med (Zagreb)</addtitle><date>2020-06-15</date><risdate>2020</risdate><volume>30</volume><issue>2</issue><spage>020101</spage><epage>195</epage><pages>020101-195</pages><artnum>020101</artnum><issn>1330-0962</issn><issn>1846-7482</issn><eissn>1846-7482</eissn><abstract>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.</abstract><cop>ZAGREB</cop><pub>Croatian Soc Medical Biochemistry & Laboratory Medicine</pub><pmid>32292278</pmid><doi>10.11613/BM.2020.020101</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-6073-3404</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Alkaline Phosphatase - analysis Alkaline Phosphatase - metabolism Antigens, Neoplasm - analysis bias Clinical Laboratory Techniques - methods Clinical Laboratory Techniques - standards Creatinine - analysis Diagnostic Errors Humans Lessons in Biostatistics Life Sciences & Biomedicine measurement uncertainty Medical Laboratory Technology Quality Control Science & Technology Testosterone - analysis total error Uncertainty |
title | The top-down approach to measurement uncertainty: which formula should we use in laboratory medicine? |
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