Abstract 18662: Ablation of Regions of Very Slow Conduction Within a Myocardial Scar and Recurrence of Ventricular Tachycardia After Catheter Ablation
Bibliographic record
Abstract
Background: It has been established that areas of slow conduction within a myocardial scar identified by isochronal mapping during sinus rhythm harbor the functional substrate that is involved in sustaining ventricular tachycardia (VT). We sought to test the hypothesis that targeting the region of slowest conduction during sinus rhythm would reduce VT recurrence following ablation. Methods and Results: 32 subjects underwent ablation for sustained monomorphic VT associated with structural heart disease from 2013 to 2014. Sustained VT recurred in 12 patients (37.5%). Isochronal late activation maps were created to display activation during sinus rhythm in the region of bipolar scar. The scar was divided into three zones of equal activation time. The zone with the densest isochrones was designated as having the slowest conduction . We retrospectively analyzed isochronal maps and measured the proportion of the slowest zone that was ablated (median 14%, IQR 0-50). During a mean follow-up of 6 months, recurrence of ventricular arrhythmia was significantly associated with ablation of the slowest zone (OR 0.126, CI 0.024-0.68, p 0.016). Furthermore, univariate logistic regression demonstrated reduction of 30% in the 6-month VT recurrence rate for every 10% increase in percent of the slowest zone ablated (OR 0.7, 95% CI 0.5-1.0, p=0.05). Conclusions: Patients who had ablation in the region of slowest conduction were significantly less likely to have recurrence of ventricular tachycardia. Our data suggests a strategy to target the slowest region of conduction for substrate modification may hold promise for improving outcomes of scar-mediated VT ablation.
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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) | 0.002 | 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".