title: 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.
abstract: 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.
authors.affiliation: Division of Emergency Medicine, Department of Clinical Sciences, Skåne University Hospital at Lund, Sweden. jakob@lundager.eu
authors.forename: Jakob L
authors.initials: JL
authors.lastname: Forberg
authors.forename: Ardavan
authors.initials: A
authors.lastname: Khoshnood
authors.forename: Michael
authors.initials: M
authors.lastname: Green
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
authors.forename: Jonas
authors.initials: J
authors.lastname: Björk
authors.forename: Stefan
authors.initials: S
authors.lastname: Jovinge
authors.forename: Lars
authors.initials: L
authors.lastname: Edenbrandt
authors.forename: Ulf
authors.initials: U
authors.lastname: Ekelund
mesh_terms: D000368:Aged; D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000706:statistics & numerical data; D004632:Emergency Medical Services; D005260:Female; D006801:Humans; D008297:Male; D009203:Myocardial Infarction / Q000175:diagnosis*; D017202:Myocardial Ischemia / Q000175:diagnosis; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results