Sudden cardiac death in congenital heart disease
Bibliographic record
Abstract
Sudden cardiac death (SCD) accounts for up to 25% of deaths in patients with congenital heart disease (CHD). To date, research has largely been driven by observational studies and real-world experience. Drawbacks include varying definitions, incomplete taxonomy that considers SCD as a unitary diagnosis as opposed to a terminal event with diverse causes, inconsistent outcome ascertainment, and limited data granularity. Notwithstanding these constraints, identified higher-risk substrates include tetralogy of Fallot, transposition of the great arteries, cyanotic heart disease, Ebstein anomaly, and Fontan circulation. Without autopsies, it is often impossible to distinguish SCD from non-cardiac sudden deaths. Asystole and pulseless electrical activity account for a high proportion of SCDs, particularly in patients with heart failure. High-quality cardiopulmonary resuscitation is essential to improve outcomes. Pulmonary hypertension and CHD complexity are associated with lower likelihood of successful resuscitation. Risk stratification for primary prevention implantable cardioverter-defibrillators (ICDs) should consider the probability of SCD due to a shockable rhythm, competing causes of mortality, complications of ICD therapy, and associated costs. Risk scores to better estimate probabilities of SCD and CHD-specific guidelines and consensus-based recommendations have been proposed. The subcutaneous ICD has emerged as an attractive alternative to transvenous systems in those with vascular access limitations, prior device infections, intra-cardiac shunts, or a Fontan circulation. Further improving SCD-related outcomes will require a multidimensional approach to research that addresses disease processes and triggers, taxonomy to better reflect underlying pathophysiology, high-risk features, early warning signs, access to high-quality cardiopulmonary resuscitation and specialized care, and preventive therapies tailored to underlying mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".