AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Developing a national surveillance system for stroke and acute myocardial infarction using claims data in the Republic of Korea: a retrospective study.

BACKGROUND: Limited information is available concerning the epidemiology of stroke and acute myocardial infarction (AMI) in the Republic of Korea. This study aimed to develop a national surveillance system to monitor the incidence of stroke and AMI using national claims data. METHODS: We developed and validated identification algorithms for stroke and AMI using claims data. This validation involved a 2-stage stratified sampling method with a review of medical records for sampled cases. The weighted positive predictive value (PPV) and negative predictive value (NPV) were calculated based on the sampling structure and the corresponding sampling rates. Incident cases and the incidence rates of stroke and AMI in the Republic of Korea were estimated by applying the algorithms and weighted PPV and NPV to the 2018 National Health Insurance Service claims data. RESULTS: In total, 2,200 cases (1,086 stroke cases and 1,114 AMI cases) were sampled from the 2018 claims database. The sensitivity and specificity of the algorithms were 94.3% and 88.6% for stroke and 97.9% and 90.1% for AMI, respectively. The estimated number of cases, including recurrent events, was 150,837 for stroke and 40,529 for AMI in 2018. The age- and sex-standardized incidence rate for stroke and AMI was 180.2 and 46.1 cases per 100,000 person-years, respectively, in 2018. CONCLUSION: This study demonstrates the feasibility of developing a national surveillance system based on claims data and identification algorithms for stroke and AMI to monitor their incidence rates.