Left atrial appendage isolation in atrial fibrillation catheter ablation: A meta‐analysis
Bibliographic record
Abstract
Abstract A significant proportion of patients’ experience recurrence of atrial fibrillation (AF) despite pulmonary venous isolation (PVI), especially those with persistent AF. Isolation of the left atrial appendage (LAA) may reduce AF recurrence. The aim of this study was to assess the efficacy of LAA isolation in addition to PVI compared with PVI alone. We conducted a comprehensive search of electronic databases, up to April 21st, 2017, for all studies comparing the effect LAA electrical isolation or ligation in addition to PVI, as opposed to PVI alone, on the recurrence of atrial fibrillation after catheter ablation. We used random‐effects meta‐analysis models to summarize the studies. One RCT and four observational studies enrolling 781 patients were retained. Four studies assessed the added effect of LAA catheter ablation, and one study evaluated the effect of LAA ligation with the aim of LAA electrical isolation. Four studies exclusively enrolled patients with persistent atrial fibrillation and one study predominantly enrolled patients with persistent atrial fibrillation. Follow‐up ranged from 12 to 15 months. The addition of LAA isolation to PVI reduced AF recurrence compared with the latter alone (odds ratio (OR) = 0.19; 95% confidence intervals (CI) = 0.10‐0.37; P < 0.00001). Left atrial appendage isolation was also associated with a reduction in AF recurrence after repeat ablation (OR = 0.40; CI = 0.25‐0.65; P = 0.0003). The addition of LAA isolation to PVI was associated with a decrease in AF recurrence in patients with persistent AF. Further studies are needed to assess the effect on long‐term risk of stroke.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".