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

Nyström, Axel LU ; Olsson de Capretz, Pontus LU ; Björkelund, Anders LU ; Lundager Forberg, Jakob LU ; Ohlsson, Mattias LU orcid ; Björk, Jonas LU and Ekelund, Ulf LU orcid (2024) In Journal of Electrocardiology 82. p.42-51
Abstract
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.... (More)
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. (Less)
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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Electrocardiology
volume
82
pages
42 - 51
publisher
Elsevier
external identifiers
  • pmid:38006763
  • scopus:85182224199
ISSN
1532-8430
DOI
10.1016/j.jelectrocard.2023.11.002
project
AIR Lund - Artificially Intelligent use of Registers
language
English
LU publication?
yes
id
7d494e5b-91a0-4e58-a76c-952766793e4a
date added to LUP
2024-01-02 12:25:02
date last changed
2024-04-17 13:23:33
@article{7d494e5b-91a0-4e58-a76c-952766793e4a,
  abstract     = {{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.}},
  author       = {{Nyström, Axel and Olsson de Capretz, Pontus and Björkelund, Anders and Lundager Forberg, Jakob and Ohlsson, Mattias and Björk, Jonas and Ekelund, Ulf}},
  issn         = {{1532-8430}},
  language     = {{eng}},
  pages        = {{42--51}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Electrocardiology}},
  title        = {{Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients}},
  url          = {{http://dx.doi.org/10.1016/j.jelectrocard.2023.11.002}},
  doi          = {{10.1016/j.jelectrocard.2023.11.002}},
  volume       = {{82}},
  year         = {{2024}},
}