Publication Date
7-1-2023
Journal
Nature Medicine
DOI
10.1038/s41591-023-02396-3
PMID
37386246
PMCID
PMC10353937
PubMedCentral® Posted Date
6-29-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Emergency Service, Hospital, Time Factors, Myocardial Infarction, Electrocardiography, Risk Assessment, Translational research, Myocardial infarction, Machine learning
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 relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
Included in
Medical Biochemistry Commons, Medical Biophysics Commons, Medical Genetics Commons, Medical Specialties Commons, Translational Medical Research Commons
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