Implantable Cardioverter‐Defibrillators in Patients with Left Ventricular Noncompaction
Bibliographic record
Abstract
BACKGROUND: Left ventricular noncompaction (LVNC) is a rare, congenital cardiomyopathy and can be associated with heart failure, embolic events, arrhythmias, and sudden cardiac death. Implantation of implantable cardioverter-defibrillators in these patients is a treatment option, but data on long-term follow-up are limited. The aim of the study was to analyze the clinical outcome of patients with LVNC who were treated with an implantable cardioverter-defibrillator (ICD). METHODS: We conducted a retrospective study on 12 patients (mean age: 45 +/- 13 years, range 20-60) with LVNC, who underwent ICD implantation for secondary (n = 8) and primary (n = 4) prevention. RESULTS: During a median follow-up of 36 months, five patients (42%) presented with appropriate ICD therapy: in four of the eight patients (50%) in whom the ICD was implanted as a secondary prevention and in one of the four patients (25%) for whom the ICD was implanted for primary prevention. In eight patients (66%) supraventricular tachyarrhythmias were documented. Improvement of left ventricular function could be observed in one of two patients with a biventricular ICD. CONCLUSIONS: Potentially life-threatening ventricular tachyarrhythmias may occur in patients with LVNC. ICD therapy may be effective for primary and secondary prevention in these patients. Due to the high prevalence of supraventricular tachyarrhythmias devices with reliable detection enhancements should be considered.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".