The Role of Ablation in Prevention of Recurrent Implantable Cardioverter Defibrillator Shocks in Patients With Tetralogy of Fallot
Bibliographic record
Abstract
Background Implantable cardioverter defibrillators (ICDs) are effective in preventing arrhythmic sudden cardiac death in patients with tetralogy of Fallot (TOF). Although ICD therapies for malignant ventricular arrhythmias can be life-saving, shocks could have deleterious consequences. Substrate-based ablation therapy has become the standard of care to prevent recurrent ICD shocks in patients with ischemic cardiomyopathy. However, the efficacy and safety of this invasive therapy in the prevention of recurrent ICD shocks in patients with TOF has not been well evaluated. Methods Records of a total of 47 consecutive TOF patients (mean age: 43.1 ± 13.2 years, male sex: n = 34 [72.3%]) who underwent ICD implantation for secondary prevention between 2000 and 2018 were reviewed. Results Twenty (42.6%) patients underwent invasive therapy (radiofrequency catheter ablation, n = 8; surgical ablation with pulmonary valve replacement, n = 12) before ICD implantation. Twenty-seven patients (57.4%) were managed noninvasively. During follow-up (median 80.5 [interquartile range, 28.5-131.0] months), 2 (10.0%) patients in the invasive group and 10 (37.0%) patients in the noninvasive group received appropriate ICD shocks ( P = 0.036). Logistic regression analysis showed that invasive therapy was associated with a decreased risk of ICD shocks by 81.1% (odds ratio, 0.189; 95% confidence interval, 0.036-0.990; P = 0.049). Furthermore, invasive therapy was associated with decreased risk of the composite outcomes of ICD shock, death, cardiac transplantation, and hospital admission (odds ratio, 0.090; 95% confidence interval, 0.025-0.365; P = 0.013) compared with noninvasive therapy. Conclusions Invasive substrate modification therapy was associated with a lower likelihood of ICD shocks and improvement of long-term outcomes in TOF patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".