Atrial Fibrillation Recurrence After Left Atrial Appendage Occlusion in Patients Undergoing Ablation
Bibliographic record
Abstract
BACKGROUND: It remains unclear whether concomitant or subsequent left atrial appendage closure (LAAC) may affect atrial fibrillation (AF) ablation outcomes. OBJECTIVES: This study sought to evaluate the impact of LAAC on AF recurrence and its role in clinical outcomes. METHODS: A subanalysis was conducted on 1,600 patients from the randomized OPTION (Comparison of Anticoagulation With Left Atrial Appendage Closure After AF Ablation) trial. Patients were stratified into 2 groups: anticoagulation (no device) and LAAC. The primary endpoint was AF recurrence, and the secondary endpoint was stroke or systemic embolism occurring after 12 months of follow-up. Patients were categorized based on AF recurrence (yes/no) at 12 months. Arrhythmia recurrence was assessed using device interrogations in patients with cardiac implantable electronic devices and 12-lead electrocardiograms in those without devices. RESULTS: Of the total cohort, 803 patients (50%) underwent LAAC (41% concomitant, 59% sequential), and 797 patients (50%) were in the control group (no device). Baseline characteristics were balanced across both treatment arms. At 36-month follow-up, AF recurrence rates did not differ significantly between the groups (LAAC: 49.2% vs no LAAC: 45.5%; P = 0.08). No difference was found between sequential and concomitant LAAC (43.6% vs 43.9%, respectively; P = 0.83). Stroke and systemic embolism rates were low and comparable between patients with and without AF recurrence (no AF: 1.00%, AF recurrence: 1.30%; P = 0.57). CONCLUSIONS: Among patients undergoing AF ablation, LAAC (concomitant or sequential) does not affect AF ablation outcomes. The stroke/systemic embolism rate was low in all groups. Nevertheless, the trial highlights that stroke and systemic embolism remain significant risks, even in patients without AF recurrence, and should not be overlooked.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".