Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

Dung-Jang Tsai; Yu-Sheng Lou; Chin-Sheng Lin; Wen-Hui Fang; Chia-Cheng Lee; Ching-Liang Ho; Chih-Hung Wang; Chin Lin

doi:10.1177/20552076231187247

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

Digit Health. 2023 Jul 10:9:20552076231187247. doi: 10.1177/20552076231187247. eCollection 2023 Jan-Dec.

Authors

Dung-Jang Tsai^{1

2

3

4}, Yu-Sheng Lou^{2

3

5}, Chin-Sheng Lin⁶, Wen-Hui Fang⁷, Chia-Cheng Lee^{8

9}, Ching-Liang Ho¹⁰, Chih-Hung Wang^{11

12}, Chin Lin^{2

3

4

5}

Affiliations

¹ Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City.
² Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei.
³ Graduate Institutes of Life Sciences, Tri-Service General Hospital, National Defense Medical Center, Taipei.
⁴ Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei.
⁵ School of Public Health, National Defense Medical Center, Taipei.
⁶ Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei.
⁷ Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei.
⁸ Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei.
⁹ Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei.
¹⁰ Division of Hematology and Oncology, Tri-Service General Hospital, National Defense Medical Center, Taipei.
¹¹ Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei.
¹² Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei.

Abstract

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.

Keywords: Artificial intelligence; cardiovascular disease; deep learning; electrocardiogram; electronic health record; mortality; risk stratification.