This study is a prospective multicenter observational study for external validation and model advancement of a deep learning based 12-lead electrocardiogram analysis algorithm targeting adult patients presenting to the emergency department with chest pain and acute myocardial infarction equivalent symptoms. About 9,000 adult patients will be enrolled at 20 emergency medical centers in Korea. Artificial intelligence algorithms are manufactured by Medical AI Co., Ltd. It is an advanced version based on the model developed and published in 2020. It had the diagnostic performance of area under the receiver operating curve 0.901 and 0.951 for acute myocardial infarction and ST-segment elevation myocardial infarction, respectively. The primary endpoint is a diagnosis of acute myocardial infarction on the day of the emergency center visit, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration will begin at centers that have been approved by the Institutional Review Board. This is the first prospective multicenter emergency department validation study for a 12-lead electrocardiogram artificial intelligence algorithm to diagnose acute myocardial infarction. This study will give insight into the direction of future development by verifying whether the deep learning algorithm works well for patients visiting the real-world adult emergency medical center.
Study Type
OBSERVATIONAL
Enrollment
8,814
CHA Bundang Medical Center
Seongnam, South Korea
Diagnosis of acute myocardial infarction (Type 1, 2)
Accuracy metrics include area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value, along with a 95% confidence interval.
Time frame: Index admission
Major adverse cardiovascular event (MACE)
MACE is defined as death, myocardial infarction, stroke, target-vessel revascularization, or stent thrombosis occurring within 30 days of index visit. Accuracy metrics include area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value, along with a 95% confidence interval.
Time frame: 30-day after index admission
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