Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity.…

• NI
  National Institutes of Health

  Awards: KL2TR002490, R01HL137761, UL1TR001857
• NH
  National Heart, Lung, and Blood Institute

  Award: R01HL137761
• NI
  National Institute of Nursing Research

  Award: K23NR017896
• NC
  National Center for Advancing Translational Sciences

  Awards: UL1TR001857, KL2TR002490