Risk stratification for sudden death in congenital heart disease: bridging evidence, uncertainty, and individual decision-making.
Authors
Affiliations (2)
Affiliations (2)
- Electrophysiology Service and Adult Congenital Heart Disease Center.
- Division of Statistics, Montreal Health Innovations Coordinating Center (MHICC), Montreal Heart Institute, Université de Montréal, Montreal, Canada.
Abstract
Sudden cardiac death (SCD) remains a feared and difficult-to-predict outcome in patients with congenital heart disease (CHD). This review examines the latest evidence in risk stratification, with a focus on limitations of existing models and the mechanistic and statistical complexities that hinder individualized decision-making. New multivariable risk scores for repaired tetralogy of Fallot and systemic right ventricle have improved prognostic resolution. Artificial intelligence-enabled ECG algorithms have shown promise in early identification of high-risk individuals with repaired tetralogy of Fallot. In parallel, three-dimensional cardiac magnetic resonance imaging has been leveraged to delineate arrhythmogenic isthmuses, enhancing substrate-guided interventions. While these tools enhance risk estimation, they require validation specific to the prediction of shockable terminal rhythms, improved interpretability, and integration into individualized decision frameworks. SCD risk prediction in CHD is evolving toward a multimodal, individualized approach that emphasizes probabilistic reasoning, shared decision-making, and epistemic humility. Although new models and technologies offer incremental gains, they do not eliminate the uncertainty inherent in predicting rare events. The application of population-based tools to individual patients must be interpreted cautiously, recognizing that SCD represents a final common pathway for diverse pathophysiological processes, and that decisions about ICD implantation entail complex trade-offs.