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Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
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Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
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Journal Article
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
2023
Overview
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.
A machine learning algorithm, developed to detect occlusion myocardial infarction with no-ST elevation from electrocardiogram, outperforms clinicians in diagnostic assessments.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
