An AI-enhanced ECG model predicted 5-year mortality in congenital heart disease patients with an area under the receiver operating characteristic curve of 0.79 (95% CI 0.77-0.81).
Cohort (n=79,568)
No
Does an artificial intelligence-enhanced electrocardiogram tool predict 5-year mortality better than standard clinical metrics in patients with congenital heart disease?
An AI-enhanced ECG model can effectively predict 5-year mortality in patients with congenital heart disease across all ages, outperforming traditional risk markers like LVEF and QRS duration.
Effect estimate: AUC 0.79 (95% CI 0.77-0.81)
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.
Mayourian et al. (Thu,) conducted a cohort in Paediatric and adult congenital heart disease (n=79,568). Artificial intelligence-enhanced electrocardiogram (ECG) tool vs. Age at ECG, QRS duration, and left ventricular ejection fraction was evaluated on 5-year mortality (AUC 0.79, 95% CI 0.77-0.81). An AI-enhanced ECG model predicted 5-year mortality in congenital heart disease patients with an area under the receiver operating characteristic curve of 0.79 (95% CI 0.77-0.81).
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