Abstract 15176: Artificial Intelligence Model Detecting Acute Myocardial Infarction Requiring Revascularization

Background: Rapid myocardial revascularization in patients with acute myocardial infarction (AMI) is essential for increasing the chances and extent of myocardial salvage and better clinical outcomes. There still have been largely missed opportunities in the timely diagnosis of AMI. Research Questions: Can artificial intelligence (AI) be helpful in timely diagnosing AMI? Aims: We aimed to develop an AI algorithm for detecting AMI requiring revascularization using electrocardiograms (ECGs). Methods: We developed an AI model detecting myocardial infarction requiring revascularization from a 12-lead ECG. This retrospective cohort study included 17,907 ECGs from 5,872 coronary angiography-validated AMI patients and 705,482 ECGs from 294,755 non-AMI patients at Chonnam National University Hospital between May 2013 and December 2020. External validation was performed at Hallym University Kangnam Sacred Heart Hospital using 357,072 ECGs from 163,870 patients between January 2006 and December 2020. Results: The prevalence rates of AMI-ECGs were 5.29% (1356/25,627) and 0.87% (3089/357,072) in the internal test and external validation set, respectively. The areas under the receiver operating characteristic curves (AUROC) were 0.958 (95% CI, 0.955 to 0.962), 0.988 (95% CI, 0.986 to 0.991), and 0.930 (95% CI, 0.923 to 0.936) in total, ST-elevation myocardial infarction, and non-ST-elevation myocardial infarction in the external validation, respectively (Figure 1A). Subgroup analysis assessing the efficacy of subsets of 12-lead ECG revealed that the AUROC were 0.926 (95% CI, 0.920 to 0.931), 0.921 (95% CI, 0.915 to 0.926), and 0.846 (95% CI, 0.839 to 0.854) in 6 limb-lead, lead combining I and II, and lead I, respectively (Figure 1B). Conclusion: Our AI model detecting myocardial infarction can be beneficial in identifying patients with acute myocardial infarction requiring revascularization timely.