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Published on: December 11, 2019
Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases
Dung-Jang Tsai1,2,3,4, Yu-Sheng Lou2,3,5, Chin-Sheng Lin6
1Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City.
Insights
Artificial intelligence (AI) developed a deep learning model (DLM) using electrocardiograms (ECG) to predict cardiovascular disease (CVD) mortality. This AI-enabled ECG identifies high-risk patients for early intervention.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Electrocardiogram (ECG) is a primary tool for cardiovascular disease (CVD) management.
- Existing artificial intelligence (AI) applications in ECG lack integrated CVD risk stratification.
- Mortality is a critical global health outcome for CVD risk assessment.
Purpose of the Study:
- To develop a survival deep learning model (DLM) using ECG data.
- To establish a critical ECG value for CVD risk stratification.
- To explore the association between ECG-derived risk and various CVD events and mortality.
Main Methods:
- Trained a DLM on 451,950 12-lead resting ECGs from 210,552 patients.
- Validated the DLM internally and externally using multiple patient cohorts and datasets.
- Investigated associations with all-cause mortality, cardiovascular mortality, acute myocardial infarction (AMI), stroke (STK), and heart failure (HF).
Main Results:
- The DLM achieved high predictive performance (C-indices of 0.858/0.836 in internal/external validation).
- High-risk individuals identified by the DLM showed significantly increased hazard ratios for mortality and CVD events (e.g., HR 14.16 for overall mortality).
- Consistent results were observed across internal, external, and transnational validation cohorts.
Conclusions:
- AI-enabled ECG can serve as a passive tool for electronic health record-based CVD risk screening.
- This approach may identify asymptomatic and previously unaware high-risk patients.
- The DLM provides a critical indicator for CVD risk stratification and mortality prediction.
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.
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