Impact of catheter ablation for ventricular tachycardia on left ventricular ejection fraction in patients with structural heart disease
Bibliographic record
Abstract
Abstract Background Catheter ablation (CA) is efficacious for the treatment of ventricular tachycardia (VT) in patients with structural heart disease; however, heart failure contributes to long‐term mortality in this cohort. Whether CA worsens left ventricular function requires investigation. Methods We retrospectively analyzed 142 consecutive patients with structural heart disease undergoing CA for VT. Pre‐ablation left ventricular ejection fraction (LVEF) was compared to LVEF postablation, predictors of change in LVEF were identified, and the relationship between change in LVEF and arrhythmic recurrence was assessed. Results Patients with ischemic cardiomyopathy (ICM) had lower pre‐ablation LVEF than patients with non‐ischemic cardiomyopathy (NICM) (36.2 ± 14.3% vs. 50.8 ± 12.8%, p < 0.001). There was no statistically significant change in LVEF following ablation for patients with ICM ( p = 0.45) or NICM ( p = 0.75). Patients with pre‐ablation LVEF ≤20% experienced the largest recovery in LVEF, mean recovery 5.3% (95% CI: 0.6–10.1), p = 0.03, with LVEF recovery postablation similar in ICM and NICM patients ( p = 0.69). Recovery of LVEF was associated with a decreased incidence of ventricular arrhythmia (VA) recurrence ( p = 0.03) and an increased VA‐recurrence‐free survival ( p = 0.04). Conclusion CA for VT does not cause a decline in LVEF among patients with structural heart disease. The subset of patients with severely impaired LVEF may experience an increase in LVEF following ablation and an associated reduction in VA recurrence.
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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 | 0.000 | 0.002 |
| 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".