Long‐term risk of stroke and bleeding post–atrial fibrillation ablation
Bibliographic record
Abstract
BACKGROUND: Catheter ablation (CA) is an established therapy for atrial fibrillation (AF). Studies regarding long-term real-world outcomes post-CA have inconsistently accounted for oral anticoagulation (OAC). OBJECTIVES: To describe patterns of OAC use post-CA and to compare the OAC-adjusted long-term risk of stroke and major bleeding in AF patients with and without CA. METHODS: A population-based cohort of AF patients was constructed in Quebec and Ontario, Canada (1999-2014). Propensity score matching was performed to determine the incidence rates of stroke and major bleeding among those undergoing CA, adjusted for time-dependent OAC use. RESULTS: From the entire cohort, 6391 patients were identified as having undergone CA as compared to 482 977 patients who did not. Of these, 1240 patients with government medical insurance undergoing CA were matched with 2427 patients without CA. Post-CA, 78%, 65%, and 61% remained on an OAC at 1, 2, and 5 years, while 75%, 71%, and 68% of patients not undergoing CA were on OACs at 1, 2, and 5 years. At follow-up, there was no statistically significant difference for stroke (adjusted hazard ratio [HR], 0.88; 95% CI, 0.63 to 1.21) or major bleeding (adjusted HR, 0.88; 95% CI, 0.73 to 1.06). CONCLUSION: No evidence was found that CA significantly decreases the risk of stroke or major bleeding when adjusting for OAC use over time. It may be prudent to continue anticoagulation post-CA based on patient-risk profile until randomized trials demonstrate both reduced stroke rates with CA, and improved safety (balancing stroke and bleeding risk) with OAC discontinuation post-CA.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".