Implantable Cardioverter-Defibrillator Therapy in Patients With Transposition of the Great Arteries: A Systematic Review of the Literature
Bibliographic record
Abstract
Background: Patients with a systemic right ventricle (sRV) due to transposition of the great arteries are known to have a particularly high risk of sudden cardiac death. Current guidelines issue a weak recommendation to consider primary prevention implantable cardioverter-defibrillator (ICD) implantation in patients with severe sRV dysfunction. This systematic review aims to ascertain factors that are associated with appropriate ICD therapy in patients with an sRV and primary prevention ICD, so that we can further refine selection criteria for implantation in this population. Methods: A systematic search of MEDLINE and Embase was performed to identify all studies that explored ICD therapies and associated clinical characteristics in patients with an sRV and primary prevention ICD from the inception of the databases until February 14, 2023. Results: A total of 11 articles were included in the final analysis. Among those with a primary prevention ICD, 23 (9.1%) had appropriate ICD therapies and 48 (19%) received inappropriate ICD therapies. Among those with appropriate ICD therapies, the most common reason for implantation was sRV dysfunction, followed by nonsustained ventricular tachycardia and ventricular tachycardia on a Holter monitor. Among those with a secondary prevention ICD, 15 (34.9%) received appropriate ICD therapies and 5 (11%) had inappropriate ICD therapies. Most inappropriate therapies were due to atrial tachyarrhythmias. Conclusions: sRV dysfunction was the most consistently reported risk factor for appropriate ICD therapy in our review. Effective treatment of atrial tachyarrhythmias remains a priority. Larger scale studies are required to develop and validate risk calculation in this population.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".