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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction

Author

  • Jakob L Forberg
  • Ardavan Khoshnood
  • Michael Green
  • Mattias Ohlsson
  • Jonas Björk
  • Stefan Jovinge
  • Lars Edenbrandt
  • Ulf Ekelund

Summary, in English

Background: Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI. Methods: Five hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care were prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not) and the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty register. The CCU physician's real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not) were registered for comparison. Results: The ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and 99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN's receiver operating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute PCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were theoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by 64% without missing any case with STEMI or a need of immediate PCI. Conclusions: Our ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs, and has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.

Department/s

  • Computational Biology and Biological Physics
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • eSSENCE: The e-Science Collaboration
  • EpiHealth: Epidemiology for Health

Publishing year

2012

Language

English

Pages

1-9

Publication/Series

Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine

Volume

20

Issue

1

Document type

Journal article

Publisher

BioMed Central (BMC)

Topic

  • Anesthesiology and Intensive Care
  • Cardiac and Cardiovascular Systems
  • Other Engineering and Technologies not elsewhere specified
  • Other Clinical Medicine

Keywords

  • Myocardial Infarction
  • STEMI
  • ST Elevation Myocardial Infarction
  • Artificiell Intelligens
  • Diagnosis
  • ECG
  • Electrocardiogram

Status

Published

Research group

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Other

  • ISSN: 1757-7241