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.

Journal: The Lancet. Digital health
PMID: 39214763

Abstract

BACKGROUND: Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) assays have been shown to provide similar analytical precision despite substantially shorter turnaround times compared with laboratory-based hs-cTn assays. We applied the previously developed machine learning based personalised Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) algorithm, which can predict the individual probability of myocardial infarction, with a single POC hs-cTn measurement, and compared its diagnostic performance with standard-of-care pathways for rapid rule-out of myocardial infarction.

Authors

  • Betül Toprak
    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.
  • Hugo Solleder
    Cardio-CARE, Medizincampus Davos, Davos, Switzerland.
  • Eleonora Di Carluccio
    Cardio-CARE, Medizincampus Davos, Davos, Switzerland.
  • Jaimi H Greenslade
    Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
  • William A Parsonage
    Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia.
  • Karen Schulz
    Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
  • Louise Cullen
    Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.
  • Fred S Apple
    Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA; Hennepin Healthcare Research Institute, Minneapolis, MN, USA.
  • Andreas Ziegler
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
  • Stefan Blankenberg
    Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.

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