Incidence and Significance of Early Recurrences Associated with Different Ablation Strategies for AF: A STAR‐AF Substudy
Bibliographic record
Abstract
BACKGROUND: Early recurrences of atrial tachyarrhythmias (ERAT) are common after atrial fibrillation (AF) ablation, and predict late recurrences (LR). We sought to determine the impact of different ablation strategies on ERAT and LR. METHODS AND RESULTS: The STAR-AF trial randomized 100 patients with paroxysmal or persistent AF to ablation of complex fractionated electrograms (CFAE) alone, pulmonary vein isolation (PVI) alone, or combined PVI + CFAE. Patients were followed for 12 months. ERAT was defined as any recurrence of AF, atrial tachycardia, or flutter (AT/AFL) >30 seconds during the first 3 months of follow-up. LR was defined as any recurrence of AF/AT/AFL >30 seconds 3-12 months post. Forty-nine patients experienced ERAT. The index ablation strategy was the only independent predictor of ERAT on multivariate analysis (HR 2.24 PVI vs PVI + CFAE; and HR 2.65 CFAE vs PVI + CFAE). Fifty-two patients experienced LR. The presence of ERAT (HR 3.23), the use of antiarrhythmic drug (AAD) in the first 3 months postablation (HR 2.85), and the index ablation strategy were independently associated with LR (HR 3.42 PVI vs PVI + CFAE; HR 4.72 CFAE vs PVI + CFAE). Thirty-five of 49 (71%) patients with ERAT and 17 (33%) of 51 patients without ERAT had LR (P < 0.0001). Among patients with ERAT, increased left atrium size (HR 1.08), the use of AAD in the first 3 months postablation (HR 2.86) and the index ablation strategy were independently associated with LR (HR 4.77 PVI vs PVI + CFAE; HR 4.45 CFAE vs PVI + CFAE). CONCLUSION: ERAT is common following AF ablation and is strongly associated with LR. Although CFAE ablation alone results in higher rates of early and LR, the addition of CFAE to PVI results in increased long-term success without an increase in ERAT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".