AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial
Abstract
The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). Whi…
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AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.
“AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [56]
Cites
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Self-citationPrediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314 · see citing works
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236 · see citing works
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Masters, K., Herrmann-Werner, A., Festl-Wietek, T., & Taylor, D. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2387802 · Cited by 46 · see citing works
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- Authors
- Lin, C.-S., Liu, W.-T., Tsai, D.-J., Lou, Y.-S., Chang, C.-H., Lee, C.-C., Fang, W.-H., Wang, C.-C., Chen, Y.-Y., Lin, W.-S., Cheng, C.-C., Lee, C.-C., Wang, C.-H., Tsai, C.-S., Lin, S.-H., & Lin, C.
- Affiliations
- Tri-Service General Hospital (TW)
- Fu Jen Catholic University (TW)
- National Taiwan University (TW)
- Corresponding author
- Lin, Chin-Sheng
- Lin, Chin
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-04-29
- Volume
- 30
- Issue
- 5
- Pages
- 1461-1470
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4395959958
- Cited by
- 115
- References
- 3
- Citations by year
- 2026: 45 · 2025: 51 · 2024: 19
- FWCI
- 34.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Affairs Bureau, Taiwan https://mab.mnd.gov.tw/ National Science and Technology Council, Taiwan https://www.nstc.gov.tw/ Cheng Hsin General Hospital https://chghims.com.tw/
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, electrocardiogram, all-cause mortality, hospitalized patients, risk stratification, clinical decision support, cardiovascular mortality, intensive care utilization, early warning systems
- MeSH terms
- Aged, Artificial Intelligence, Electrocardiography, Female, Humans, Male, Middle Aged
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