Artificial Intelligence-Enabled Electrocardiogram Guidance for Pulmonary Valve Replacement Timing in Repaired Tetralogy of Fallot.

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 BCH, 101 at 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<0.001) with a higher c-index of 0.84. Survival was similar between low- and intermediate-risk groups (97-98% 15-year survival; p=0.6), with increased mortality for the high-risk group (83% 15-year survival; p=0.009). The matched cohort demonstrated that PVR was associated with increased survival overall (HR 0.28 [95% CI, 0.13-0.60], p=0.001). 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=0.04) and high-risk (HR 0.3 [0.1-0.7]; p=0.005) groups, but not in the low-risk group (p=0.8).

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.