Incremental Efficacy for Repeat Ablation Procedures for Catheter Ablation of Atrial Fibrillation
Bibliographic record
Abstract
Background: Catheter ablation atrial fibrillation (AF) is effective, but 20% to 40% of patients will require a repeat ablation. The role of more than 1 repeat ablation is not well known. Objectives: The purpose of this study was to evaluate the effectiveness and incremental benefits of multiple repeat catheter ablations to treat AF in patients. Methods: We retrospectively included patients who underwent their first, second, third, and fourth AF ablation between 2004 and 2019. They were monitored with a 24-to-48-hour Holter every 3 months postablation the first year and every 6 to 12 months thereafter. Recurrence was defined as documented atrial arrhythmia >30 seconds. Outcomes are analyzed by Kaplan-Meier curves and compared by log rank test. Results: We included a total of 2,194 patients (64% with paroxysmal and 36% with nonparoxysmal AF). Mean age was 71 ± 10 years; 67% were male. After 1 ablation, freedom from AF was 52%. Among those 1,052 patients who had recurrences, 576 (55%) underwent a second ablation, 103 (10%) underwent a third procedure, and 20 (2%) underwent a fourth. Success rates for the second, third, and fourth ablation were 57%, 60%, and 40%, respectively, at 5-year follow-up. After the second ablation, freedom from AF in our entire cohort increased from 52% to 66%, with marginal changes after the third (67%) and fourth (67%) procedures. Conclusions: Although repeated ablations demonstrated significant benefits at the individual level, the success rate may drop off after a third. The overall success of the initial cohort was not significantly influenced by the success rates of multiple follow-up ablations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".