Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

Journal: Prehospital emergency care
PMID: 39235330

Abstract

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention (PPCI). Many emergency medical services (EMS) activate catheterization labs to reduce time to PPCI, but suffer a high burden of inappropriate activations. Artificial intelligence (AI) algorithms show promise to improve electrocardiogram (ECG) interpretation. The primary objective was to evaluate the potential of AI to reduce false positive activations without missing OMI.

Authors

  • Peter O Baker
    Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota.
  • Shifa R Karim
    Baylor University, Waco, Texas.
  • Stephen W Smith
    Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA.
  • H Pendell Meyers
    Department of Emergency Medicine, Carolinas Medical Center, Charlotte, North Carolina, USA.
  • Aaron E Robinson
    Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota.
  • Ishmam Ibtida
    Division of Cardiology, Stony Brook University, Stony Brook, New York.
  • Rehan M Karim
    Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota.
  • Gabriel A Keller
    Hennepin EMS, Hennepin Healthcare, Minneapolis, Minnesota.
  • Kristie A Royce
    Hennepin EMS, Hennepin Healthcare, Minneapolis, Minnesota.
  • Michael A Puskarich
    Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota.

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