Artificial Intelligence–Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings
Authors' conclusion
A serial AI-ECG model provides accurate, clinically interpretable prediction of 1-year mortality and may support risk stratification in secondary care.
Study context
- Population
- Adults aged over 50 years treated in emergency department or inpatient settings between 2014 and 2019patients included
- Participants
- 13,417 adultsEvents: 4,372 (34%)
- Predictors
- AI-ECG model developed using a deep-learning framework applied to either a single index ECG or serial ECGs, including the index and 2 prior recordings
- Predicted outcome
- 1-year all-cause mortality
Model performance
As reported in the abstract
Area under the curve
Serial AI-ECG model
0.84(95% CI 0.824-0.862)
Sensitivity at a threshold of 0.24
70%
Specificity at a threshold of 0.24
80%
Positive predictive value at a threshold of 0.24
49%
Further findings
Calibration
- Good agreement between predicted and observed risks (Brier score)
- 0.123
Decision-curve analysis
- Greater net benefit than treat-all and treat-none strategies
Addition of clinical variables beyond age and sex
- Did not improve performance
Baseline characteristics
- Median age (IQR: 57-80)
- 69 years
- Women
- 45%
Original publication
Artificial Intelligence–Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings. JACC Adv. Published 17 June 2026. doi:10.
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