Anticoagulation after catheter ablation of atrial fibrillation: An unnecessary evil? A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Anticoagulation in patients with atrial fibrillation (AF) is currently based on clinical parameters (CHA2DS 2‐VASc score) that have been shown to predict cerebrovascular events (CVE). Controversy exists as to whether CVE risk persists unmodified after successful catheter ablation, as observational studies suggest a lower risk of CVE. Current guidelines recommend continued oral anticoagulation (OAC) based on the CHA 2DS 2‐VASc score risk profile. Methods We conducted a systematic literature review of all studies published up to July 31, 2018, that reported CVE after catheter ablation of AF and compared patients on or off OAC. Random‐effects models were used to demonstrate the risk of CVE and major bleeding in on‐OAC vs off‐OAC patients. This analysis was further stratified by CHADS2 and CHA 2DS 2‐VASc score. Results We retained 16 studies, 10 prospective cohort and 6 retrospective cohort, that met inclusion criteria, and which enrolled 25 177 patients: 13 166 off‐OAC and 12 011 on‐OAC. No significant difference in the incidence of CVE emerged between on‐OAC and off‐OAC patients after AF ablation (risk ratio, 0.66; confidence interval [CI], 0.38, 1.15). Similar results were found after stratification by CHADS2 and CHA 2DS 2‐VASc score. Off‐OAC patients suffered significantly less bleeding than those on OAC (RR, 0.17; CI, 0.09, 0.34). Of note, the percentage of patients with AF recurrence impacts the treatment effect in the two groups ( P = 0.001). Conclusions In this metanalysis, the risk‐benefit ratio favored the suspension of OAT after successful AF ablation even in patients at moderate‐high risk. Whether the reported results can be extended also to non‐vitamin K antagonist oral anticoagulants warrants further investigations.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".