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Risk stratification for sudden death in congenital heart disease: bridging evidence, uncertainty, and individual
Paul Khairy1,2, Stephanie Fuentes Rojas1, Sewanou Hermann Honfo2
1Electrophysiology Service and Adult Congenital Heart Disease Center.
Insights
Sudden cardiac death (SCD) risk in congenital heart disease (CHD) is challenging to predict. New tools improve risk stratification but require careful application for personalized patient care.
Area of Science:
- Cardiology
- Genetics
- Public Health
Background:
- Sudden cardiac death (SCD) is a significant concern in patients with congenital heart disease (CHD).
- Predicting SCD in this population is complex due to diverse pathophysiological processes.
- Existing risk stratification models have limitations in guiding individualized decision-making.
Purpose of the Study:
- To review the latest evidence in SCD risk stratification for patients with CHD.
- To examine limitations of current risk models and complexities hindering personalized care.
- To explore emerging technologies and approaches for improved SCD prediction.
Main Methods:
- Review of recent literature on SCD risk stratification in CHD.
- Analysis of novel multivariable risk scores and AI-enabled ECG algorithms.
- Evaluation of advanced imaging techniques like 3D cardiac MRI for substrate identification.
Main Results:
- New risk scores for specific CHD conditions (e.g., tetralogy of Fallot) enhance prognostic accuracy.
- AI-ECG shows potential for early high-risk identification in repaired tetralogy of Fallot.
- 3D cardiac MRI aids in delineating arrhythmogenic substrates for targeted interventions.
Conclusions:
- SCD risk prediction in CHD is moving towards a multimodal, individualized strategy.
- Emerging tools offer incremental improvements but do not eliminate prediction uncertainty.
- Cautious interpretation of population-based data for individual patient decisions is crucial, especially regarding ICD implantation.
Purpose Of Review:
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.
Recent Findings:
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.
Summary:
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.
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