Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease

Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool across the lifespan of a large, diverse cohort with CHD.

A convolutional neural network was trained (50%) and tested (50%) on ECGs obtained in cardiology clinic at the Boston Children's Hospital to detect 5-year mortality. Temporal validation on a contemporary cohort was performed. Model performance was evaluated using the area under the receiver operating characteristic and precision-recall curves.

The training and test cohorts composed of 112 804 ECGs (39 784 patients; ECG age range 0-85 years; 4.9% 5-year mortality) and 112 575 ECGs (39 784 patients; ECG age range 0-92 years; 4.6% 5-year mortality from ECG), respectively. Model performance (area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.77-0.81; area under the precision-recall curve 0.17, 95% confidence interval 0.15-0.19) outperformed age at ECG, QRS duration, and left ventricular ejection fraction and was similar during temporal validation. In subgroup analysis, artificial intelligence-enhanced ECG outperformed left ventricular ejection fraction across a wide range of CHD lesions. Kaplan-Meier analysis demonstrates predictive value for longer-term mortality in the overall cohort and for lesion subgroups. In the overall cohort, precordial lead QRS complexes were most salient with high-risk features including wide and low-amplitude QRS complexes. Lesion-specific high-risk features such as QRS fragmentation in tetralogy of Fallot were identified.

This temporally validated model shows promise to inexpensively risk-stratify individuals with CHD across the lifespan, which may inform the timing of imaging/interventions and facilitate improved access to care.