Risk stratification for sudden death in congenital heart disease: bridging evidence, uncertainty, and individual decision-making
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".