In Research square
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report 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, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score 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.
Al-Zaiti Salah, Martin-Gill Christian, Zègre-Hemsey Jessica, Bouzid Zeineb, Faramand Ziad, Alrawashdeh Mohammad, Gregg Richard, Helman Stephanie, Riek Nathan, Kraevsky-Phillips Karina, Clermont Gilles, Akcakaya Murat, Sereika Susan, Dam Peter Van, Smith Stephen, Birnbaum Yochai, Saba Samir, Sejdic Ervin, Callaway Clifton
2023-Jan-30
