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

Abstract

Background and aims: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Artificial intelligence; Congenital heart disease; Electrocardiogram; Mortality; Risk stratification.

Structured Graphical Abstract

A large and diverse paediatric and adult congenital heart disease cohort was used to train and test an artificial intelligence-enhanced electrocardiogram algorithm to accurately predict 5-year mortality across a range of congenital heart disease lesions. In an effort to interpret model behaviour, model explainability analysis was performed. AI-ECG, artificial intelligence-enhanced electrocardiogram; ASD, atrial septal defect; CAVC, complete atrioventricular canal defect; CNN, convoluted neural network; CoA, coarctation of the aorta; DORV, double outlet right ventricle; ECG, electrocardiogram; HLHS, hypoplastic left heart syndrome; LV, left ventricle; LVEF, left ventricular ejection fraction; PA, pulmonary atresia; RV, right ventricle; TAPVR, totally anomalous pulmonary venous return; TGA, transposition of the great arteries; ToF, tetralogy of Fallot; VSD, ventricular septal defect.

Figure 1

Kaplan–Meier survival analysis of training and testing cohorts. (A) Kaplan–Meier curve survival analysis of training (green) and testing (red) cohorts demonstrates the large, diverse cohorts across the congenital heart disease lifespan. Lesion-specific survival curves for (B) left ventricular and (C) right ventricular pathology are shown when pooling training and testing cohorts. Number at risk within each group inset below. CoA, coarctation of the aorta; DORV, double outlet right ventricle; HLHS, hypoplastic left heart syndrome; LV, left ventricular; PA, pulmonary atresia; RV, right ventricular; TAPVR, total anomalous pulmonary venous return; TGA, transposition of the great arteries; ToF, tetralogy of Fallot

Figure 2

Electrocardiogram-based deep learning model performance. (A) Artificial intelligence-enhanced electrocardiogram model performance to predict 5-year mortality evaluated using a random (blue), last (orange), and first (green) electrocardiogram per patient. (B) Performance benchmarking of the random electrocardiogram (blue) to age at electrocardiogram (orange), QRS duration (green), QTc duration (red), and left ventricular ejection fraction (purple). (C) Comparison of internal testing (blue) and temporal validation (orange) performance to predict 1-year mortality. Area under the receiver operating characteristic curve and area under the precision-recall curve metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping. AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; ECG, electrocardiogram; LVEF, left ventricular ejection fraction; PPV, positive predictive value

Figure 3

Model performance in congenital heart disease subgroups. Forest plot showing artificial intelligence-enhanced electrocardiogram area under the area under the receiver operating characteristic curve (red) and area under the precision-recall curve (black) performance when stratifying by lesion when using a random (left), last (middle), and first (right) electrocardiogram. Area under the receiver operating characteristic curve and area under the precision-recall curve metric values for each model and outcome are inset. 95% confidence intervals are shown using bootstrapping. ASD, atrial septal defect; CoA, coarctation of the aorta; CAVC, complete atrioventricular canal defect; DORV, double outlet right ventricle; ECG, electrocardiogram; HLHS, hypoplastic left heart syndrome; PA, pulmonary atresia; ToF, tetralogy of Fallot; TAPVC, total anomalous pulmonary venous connection; TGA, transposition of the great arteries; VSD, ventricular septal defect

Figure 4

Kaplan–Meier survival analysis based on artificial intelligence-enhanced electrocardiogram risk stratification. Kaplan–Meier curve survival analysis when stratifying patients as low- (blue) or high-risk (orange) based on artificial intelligence-enhanced electrocardiogram predictions using random (left), last (middle), or first (right) electrocardiogram per patient. Number at risk within each group inset below. Hazard ratio inset below (high- vs. low-risk group based on artificial intelligence-enhanced electrocardiogram predictions) with 95% confidence interval using Cox regression analysis. P-value statistic below based on log-rank testing. ECG, electrocardiogram; HR, hazard ratio

Figure 5

Lesion-specific Kaplan–Meier survival analysis based on artificial intelligence-enhanced electrocardiogram risk stratification. Lesion-specific Kaplan–Meier curve survival analysis when stratifying patients as low- (blue) or high-risk (orange) based on artificial intelligence-enhanced electrocardiogram predictions. Hazard ratio inset below (high- vs. low-risk group based on artificial intelligence-enhanced electrocardiogram predictions) with 95% confidence interval using Cox regression analysis. P-value statistic below based on log-rank testing. Initial sample size in each cohort inset. ASD, atrial septal defect; CoA, coarctation of the aorta; DORV, double outlet right ventricle; HR, hazard ratio; HLHS, hypoplastic left heart syndrome; PA, pulmonary atresia; ToF, tetralogy of Fallot; TAPVC, total anomalous pulmonary venous connection; TGA, transposition of the great arteries; VSD, ventricular septal defect

Figure 6

Explainability of artificial intelligence-enhanced electrocardiogram predictions. Visualization of median waveforms generated in each lead using electrocardiograms from the highest (red) and lowest (green) artificial intelligence-enhanced electrocardiogram predictions of the overall cohort, as well as cardiomyopathy, tetralogy of Fallot, and hypoplastic left heart syndrome subgroups. Saliency mapping demarcates regions of the electrocardiogram waveform having greatest (dark blue) and least (light blue) influence on each outcome. Saliency was averaged over the highest predicted electrocardiograms for each outcome. HLHS, hypoplastic left heart syndrome; ToF, tetralogy of Fallot