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Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.

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Title: Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.
Authors: Toprak B; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany., Solleder H; Cardio-CARE, Medizincampus Davos, Davos, Switzerland., Di Carluccio E; Cardio-CARE, Medizincampus Davos, Davos, Switzerland., Greenslade JH; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia., Parsonage WA; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia., Schulz K; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA., Cullen L; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia., Apple FS; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA; Hennepin Healthcare Research Institute, Minneapolis, MN, USA., Ziegler A; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany; Cardio-CARE, Medizincampus Davos, Davos, Switzerland; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa., Blankenberg S; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany. Electronic address: s.blankenberg@uke.de.
Corporate Authors: Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) group
Source: The Lancet. Digital health [Lancet Digit Health] 2024 Oct; Vol. 6 (10), pp. e729-e738. Date of Electronic Publication: 2024 Aug 29.
Publication Type: Journal Article; Observational Study; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101751302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2589-7500 (Electronic) Linking ISSN: 25897500 NLM ISO Abbreviation: Lancet Digit Health Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.
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  Data: <searchLink fieldCode="AU" term="%22Toprak+B%22">Toprak B</searchLink>; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany.<br /><searchLink fieldCode="AU" term="%22Solleder+H%22">Solleder H</searchLink>; Cardio-CARE, Medizincampus Davos, Davos, Switzerland.<br /><searchLink fieldCode="AU" term="%22Di+Carluccio+E%22">Di Carluccio E</searchLink>; Cardio-CARE, Medizincampus Davos, Davos, Switzerland.<br /><searchLink fieldCode="AU" term="%22Greenslade+JH%22">Greenslade JH</searchLink>; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.<br /><searchLink fieldCode="AU" term="%22Parsonage+WA%22">Parsonage WA</searchLink>; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia.<br /><searchLink fieldCode="AU" term="%22Schulz+K%22">Schulz K</searchLink>; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.<br /><searchLink fieldCode="AU" term="%22Cullen+L%22">Cullen L</searchLink>; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.<br /><searchLink fieldCode="AU" term="%22Apple+FS%22">Apple FS</searchLink>; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA; Hennepin Healthcare Research Institute, Minneapolis, MN, USA.<br /><searchLink fieldCode="AU" term="%22Ziegler+A%22">Ziegler A</searchLink>; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany; Cardio-CARE, Medizincampus Davos, Davos, Switzerland; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.<br /><searchLink fieldCode="AU" term="%22Blankenberg+S%22">Blankenberg S</searchLink>; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany. Electronic address: s.blankenberg@uke.de.
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  Data: <searchLink fieldCode="JN" term="%22101751302%22">The Lancet. Digital health</searchLink> [Lancet Digit Health] 2024 Oct; Vol. 6 (10), pp. e729-e738. <i>Date of Electronic Publication: </i>2024 Aug 29.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Ltd%22">Elsevier Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101751302 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2589-7500 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2225897500%22">25897500 </searchLink><i>NLM ISO Abbreviation: </i>Lancet Digit Health <i>Subsets: </i>MEDLINE
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        Value: 10.1016/S2589-7500(24)00191-2
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        Text: English
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              M: 10
              Text: 2024 Oct
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