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

ABSTRACT: 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. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relev

SUBMITTER: Al-Zaiti SS 

PROVIDER: S-EPMC10353937 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature