Atrial Fibrillation Inducibility After Ablation of Paroxysmal Supraventricular Tachycardia
Bibliographic record
Abstract
Background Data on the inducibility of atrial fibrillation (AF) following supraventricular tachycardia (SVT) ablation in patients without prior history of AF are limited. This study aims to identify features associated with inducible AF and the subsequent development of clinical AF in patients who undergo SVT ablation. Methods This prospective study enrolled patients who underwent electrophysiology study and SVT ablation. AF inducibility testing post ablation utilized decremental atrial burst pacing, employing the same protocol that previously had been demonstrated to have clinical significance following pulmonary vein isolation. AF was assessed clinically as well as through 12-lead electrocardiogram recordings and ambulatory Holter recordings. Results A total of 152 patients who underwent an SVT ablation were evaluated. The median age was 53 years (range: 18-90); 87 patients were female (57.2%). Atrioventricular nodal reentrant tachycardia was diagnosed in 112 of the patients (73.6%), and 40 patients (26.3%) exhibited arrhythmias related to an accessory pathway. AF was induced in 31 patients (20.4%) during the induction protocol. Among patients with inducible AF, 79% spontaneously converted to sinus rhythm, and the rest were managed with cardioversion. During a median follow-up period of 514 ± 287 days, 6 patients (3.9%) developed clinical AF. Inducible AF at the time of the SVT procedure was associated with the development of clinical AF (odds ratio=8.81, 95% confidence interval 1.53-50.63; P = 0.01). Conclusions A significant proportion of patients undergoing SVT ablation have inducible AF, but only a few have clinical AF in the first 2 years of follow-up. Inducible AF after SVT ablation predicts future AF.
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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 | 0.003 |
| 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".