Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.

Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.

Methods: A convolutional neural network was trained on electrocardiograms (ECGs) obtained at Boston Children's Hospital and tested on Boston (internal testing) and Toronto (external validation) INDICATOR (International Multicenter TOF Registry) cohorts to predict 5-year mortality. Model performance was evaluated on single ECGs per patient using area under the receiver operating (AUROC) and precision recall (AUPRC) curves.

Results: The internal testing and external validation cohorts comprised of 1,054 patients (13,077 ECGs at median age 17.8 [Q1-Q3: 7.9-30.5] years; 54% male; 6.1% mortality) and 335 patients (5,014 ECGs at median age 38.3 [Q1-Q3: 29.1-48.7] years; 57% male; 8.4% mortality), respectively. Model performance was similar during internal testing (AUROC 0.83, AUPRC 0.18) and external validation (AUROC 0.81, AUPRC 0.21). AI-ECG performed similarly to the biventricular global function index (an imaging biomarker) and outperformed QRS duration. AI-ECG 5-year mortality prediction, but not QRS duration, was a significant independent predictor when added into a Cox regression model with biventricular global function index to predict shorter time-to-death on internal and external cohorts. Saliency mapping identified QRS fragmentation, wide and low amplitude QRS complexes, and flattened T waves as high-risk features.

Conclusions: This externally validated AI-ECG model may complement imaging biomarkers to improve risk stratification in patients with rTOF.