Ventricular arrhythmia in congenital heart diseases with a systemic right ventricle
Bibliographic record
Abstract
Congenital heart disease (CHD) often involves the systemic right ventricle (SRV), which is the morphological right ventricle that supports systemic circulation. SRV patients are at a higher risk of sudden cardiac death (SCD) than other adult CHD patients and continues to be a significant cause of death in this aging population. However, the pathophysiology of ventricular arrhythmias in SRV is still not fully understood, and there may be differences between subtypes of CHD. Although these events are rare, predicting them is challenging. This review discusses contemporary strategies for assessing and preventing the risk of ventricular arrhythmias in SRV patients. Several risk factors have been identified to be associated with ventricular arrhythmias in patients with SRV. A recent risk stratification model combines independently associated factors into a risk score, and subpulmonary left ventricle dysfunction is emerging as a critical factor in risk assessment. Cardiac magnetic resonance imaging, biomarkers, and genetic data may refine the ability to predict ventricular arrhythmias in SRV. However, the question of whether implantable cardioverter-defibrillators (ICDs) should be used as a preventive measure in this cohort remains unanswered. Multicenter studies are needed to evaluate risk models and ICD use in this aging population. Given that ICDs have drawbacks, such as a high rate of inappropriate shocks and late lead-related complications, shared clinical decision-making is crucial when considering their use. The review emphasizes the need for further research in this area to improve the identification of patients at risk of clinical ventricular arrhythmias and to develop effective prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".