August 29, 2025American Heart Journal2 citationsOpen Access
View Full PaperArtificial intelligence-enabled electrocardiogram guidance for pulmonary valve replacement timing in repaired tetralogy of Fallot
Structured PICO
Does AI-ECG risk stratification predict post-PVR survival and identify which repaired tetralogy of Fallot patients benefit from pulmonary valve replacement?
P
Population
605 patients with repaired tetralogy of Fallot (rTOF) undergoing pulmonary valve replacement (PVR) with analyzable ECGs ≤3 months pre-PVR (504 at Boston Children's Hospital, 101 at Toronto General Hospital), median age 20.3 years.
I
Intervention
Artificial intelligence-enabled electrocardiogram (AI-ECG) risk stratification for 5-year mortality to guide pulmonary valve replacement (PVR)
C
Comparator
Established imaging-based model benchmark (for risk prediction) and propensity score-matched non-PVR patients (for survival benefit)
O
Outcome
Post-PVR mortality and overall survivalhard clinical
AI-ECG risk stratification successfully predicts post-PVR survival in repaired tetralogy of Fallot patients and identifies those who will derive a survival benefit from the procedure.
Abstract
BACKGROUND: Optimal timing of pulmonary valve replacement (PVR) in repaired tetralogy of Fallot (rTOF) remains challenging. We hypothesized that pre-PVR artificial intelligence-enabled electrocardiogram (AI-ECG) may inform optimal PVR timing in rTOF. METHODS: rTOF PVR patients at Boston Children's Hospital (BCH) and Toronto General Hospital (TGH) with analyzable ECGs ≤3 months pre-PVR were included. Patients undergoing PVR were propensity score-matched 1:1 to non-PVR patients. Patients were partitioned into risk tertiles based on pre-PVR AI-ECG probabilities of 5-year mortality: low-, intermediate-, and high-risk. RESULTS: The PVR cohort included 605 patients (504 at Boston Children's Hospital (BCH), 101 at Toronto General Hospital (TGH); median age 20.3 IQR, 13.6-32.0 years; median follow-up 7.5 IQR, 4.7-10.6 years; 3.6% mortality). Pre-PVR AI-ECG risk probability was predictive of post-PVR mortality (c-index 0.77), outperforming an established imaging-based model benchmark (c-index 0.70). AI-ECG remained an independent predictor when added to the benchmark model (P < .001) with a higher c-index of 0.84. Survival was similar between low- and intermediate-risk groups (97-98% 15-year survival; P = .6), with increased mortality for the high-risk group (83% 15-year survival; P = .009). The matched cohort demonstrated that PVR was associated with increased survival overall (HR 0.28 95% CI, 0.13-0.60, P = .001). Exploratory analyses stratified by risk group tertiles showed survival benefit associated with PVR in the intermediate-risk (HR 0.10 95% CI, 0.01-0.86; P = .04) and high-risk (HR 0.3 0.1-0.7; P = .005) groups, but not in the low-risk group (P = .8). CONCLUSIONS: AI-ECG predicts post-PVR survival in rTOF patients with a PVR survival benefit in intermediate- and high-risk, but not low-risk, groups. AI-ECG may complement imaging biomarkers to determine rTOF PVR timing.