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Anders Björkelund

Forskare

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Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients

Författare

  • Axel Nyström
  • Pontus Olsson de Capretz
  • Anders Björkelund
  • Jakob Lundager Forberg
  • Mattias Ohlsson
  • Jonas Björk
  • Ulf Ekelund

Summary, in English

At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice.

Avdelning/ar

  • EPI@LUND
  • Avdelningen för arbets- och miljömedicin
  • Beräkningsvetenskap för hälsa och miljö
  • Akutsjukvård
  • Centrum för miljö- och klimatvetenskap (CEC)
  • LU profilområde: Naturlig och artificiell kognition
  • eSSENCE: The e-Science Collaboration
  • Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)
  • Kirurgi och folkhälsa
  • EpiHealth: Epidemiology for Health

Publiceringsår

2024

Språk

Engelska

Sidor

42-51

Publikation/Tidskrift/Serie

Journal of Electrocardiology

Volym

82

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Cardiac and Cardiovascular Systems

Aktiv

Published

Projekt

  • AIR Lund - Artificially Intelligent use of Registers

Forskningsgrupp

  • EPI@LUND
  • Computational Science for Health and Environment
  • Emergency medicine
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Surgery and public health

ISBN/ISSN/Övrigt

  • ISSN: 1532-8430