Key result
AI-ECG high-risk classification predicts ~249% greater 3-year mortality in post-TAVR patients.
Why the study?
Long-term post-TAVR mortality remains high, and conventional risk models fail to capture subclinical electrophysiological changes linked to poor prognosis that appear on routine preoperative ECGs.
Does an AI-ECG model using preoperative electrocardiograms predict 3-year all-cause mortality in patients undergoing TAVR?
Cohort (n=711)
Yes
Does an AI-ECG model using preoperative electrocardiograms predict 3-year all-cause mortality in patients undergoing TAVR?
Hazard Ratio: 3.49 (95% CI 1.96–6.22)
Absolute Event Rate: 61.5% vs 16.5%
p-value: p=< 0.001
An AI-ECG model applied to routine preoperative electrocardiograms can accurately stratify 3-year mortality risk in patients undergoing TAVR.
AI-ECG may enhance post-TAVR mortality risk stratification; leaves open whether it improves outcomes or outperforms conventional models in prospective use.
BACKGROUND: Long-term mortality remains unsatisfactorily high after transcatheter aortic valve replacement (TAVR). Conventional risk models are limited in capturing subclinical electrophysiological alterations associated with poor prognosis, which can be identified on routine preoperative electrocardiograms. OBJECTIVES: The authors aim to develop and validate an artificial intelligence-enhanced electrocardiogram (AI-ECG) model for predicting long-term mortality in post-TAVR patients. METHODS: A total of 711 patients with severe aortic stenosis undergoing TAVR were enrolled from 2 centers. Patients from one center were divided into training and internal validation sets (7:3), and participants from another center served as the external validation cohort. Preoperative electrocardiogram images were analyzed using a Residual Network-18 model to generate mortality risk stratification. The primary endpoint was 3-year all-cause death. RESULTS: The AI-ECG model demonstrated comparable discrimination between the internal and external patient cohorts, with areas under the receiver operating characteristics curve of 0.767 (95% CI: 0.657-0.877) vs 0.712 (95% CI: 0.627-0.795) (P for DeLong test = 0.428). High-risk patients (15.5% [39 of 251]) exhibited a 61.5% (24 of 39, 95% CI: 42.8%-74.1%) 3-year mortality rate vs 16.5% (35 of 212, 95% CI: 11.4%-21.4%) in low-risk patients (84.5% [212 of 251]) (log-rank P < 0.001). Adjusted for comorbidities, high-risk classification independently predicted mortality (adjusted HR: 3.49; 95% CI: 1.96-6.22). Subgroup analysis did not reveal significant interaction effects of the AI-ECG model across different patient populations. Decision curve analysis confirmed clinical net benefit across threshold probabilities (0.05-0.60). CONCLUSIONS: The AI-ECG model provides noninvasive and accurate long-term risk stratification for TAVR patients, with promising clinical application value for individualized follow-up management.
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Shi et al. (2026) conducted a cohort in severe aortic stenosis undergoing TAVR (n=711). AI-ECG high-risk classification vs. Low-risk classification was evaluated on 3-year all-cause death (adjusted HR 3.49, 95% CI 1.96-6.22, p=< 0.001). AI-ECG high-risk classification independently predicted 3-year all-cause mortality in post-TAVR patients compared to low-risk classification (61.5% vs 16.5%; adjusted HR 3.49; 95% CI 1.96-6.22).
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