A high-mortality-risk group identified by an AI-enabled ECG deep learning model had a significantly higher risk of mortality compared to a low-risk group (HR 14.16; 95% CI 11.33-17.70).
Observational (n=506,685)
Yes
Does an AI-enabled ECG deep learning model predict mortality and cardiovascular events in patients with resting ECGs?
An AI-enabled deep learning model using standard 12-lead ECGs can effectively stratify patients for 10-year mortality and cardiovascular event risk, enabling passive EHR-based screening.
Hazard Ratio: 14.16 (95% CI 11.33–17.7)
Background: The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods: We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results: The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33-17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82-34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76-17.38), AMI (HR: 4.01, 95% CI: 2.24-7.17), STK (HR: 2.15, 95% CI: 1.70-2.72), and HF (HR: 6.66, 95% CI: 4.54-9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63-9.16) and 2.29 (95% CI: 2.15-2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions: The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.
Tsai et al. (2023) conducted an observational in Cardiovascular disease (n=506,685). High mortality risk identified by AI-enabled ECG deep learning model vs. Low-risk group was evaluated on Mortality (HR 14.16, 95% CI 11.33-17.70). A high-mortality-risk group identified by an AI-enabled ECG deep learning model had a significantly higher risk of mortality compared to a low-risk group (HR 14.16; 95% CI 11.33-17.70).