A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction.
Mathew GoebelLauren M WestaferStephanie A AyalaEl RagoneScott J ChapmanMasood R MohammedMarc R CohenJames T NiemannMarc EcksteinStephen SankoNichole BossonPublished in: Prehospital and disaster medicine (2023)
Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.
Keyphrases
- emergency medical
- st segment elevation myocardial infarction
- heart rate
- percutaneous coronary intervention
- heart rate variability
- primary care
- blood pressure
- st elevation myocardial infarction
- acute coronary syndrome
- coronary artery disease
- healthcare
- machine learning
- emergency department
- mental health
- deep learning
- atrial fibrillation
- quality improvement
- cardiac arrest
- heart failure
- neural network
- left ventricular
- health insurance
- trauma patients
- affordable care act
- monte carlo