Use of apixaban and warfarin in patients undergoing invasive procedures: insights from ARISTOTLE
Bibliographic record
Abstract
Purpose: For AF pts taking warfarin to prevent stroke, the risk of stroke or systemic embolism associated with stopping warfarin for invasive procedures is thought to be low. Little is known about apixaban use in pts undergoing invasive procedures. Methods: Using data from 18,201 pts in ARISTOTLE (median follow-up: 1.8 yrs), we described the most common invasive procedures, the use of bridging therapy, and the risk of stroke and major bleeding during the 30 days following invasive procedures. We classified procedures as major if they required general anesthesia or if the risk of significant post-operative bleeding was considered high. Procedures were also classified as emergent or non-emergent by investigators. Results: There were 11,417 invasive procedures in 6162 pts. Of these, 477 (4.2%) were major and 10,940 (95.8%) non-major; 322 (2.8%) were emergent and 11,095 (97.2%) non-emergent. The most common procedures were dental extraction/oral surgery, colonoscopy, upper endoscopy, and ophthalmic surgery. In 4082 (35.8%) procedures, study drug was not stopped. The median time of study drug stop was 4 days before the procedure for both apixaban- and warfarin-treated pts. A second "bridging" anticoagulant was used in 1335 procedures (11.7%) and was most commonly low molecular weight heparin (86.1%). Events in 30 days post-procedure by type of procedure and study drug *Percentage over the number of procedures. Conclusions: Invasive procedures are common in AF pts. The majority of procedures are non-major and non-emergent, and anticoagulation therapy was likely to be stopped peri-procedure. Overall and among emergent procedures, the rates of clinical events in the first 30 days post-procedure were low and comparable between pts receiving warfarin and apixaban.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".