Use of Apixaban and Warfarin in Patients Undergoing Procedures: Insights from ARISTOTLE (I2-2.003)
Bibliographic record
Abstract
OBJECTIVE: To examine rates of investigative procedures and subsequent risks of stroke or systemic embolism (SSE) and major bleeding in ARISTOTLE (Apixaban for Reduction In Stroke and Other Thromboembolic Events in Atrial Fibrillation). BACKGROUND: The risk of SSE associated with stopping warfarin for procedures is thought to be low; however, little is known about apixaban use in patients undergoing procedures. METHODS: Using data from 18,201 patients in ARISTOTLE (median follow-up: 1.8 yrs), we described the most common procedures, risk of stroke and major bleeding during the following 30 days, as well as use of bridging therapy. We classified procedures as “major” if they required general anesthesia or were considered to pose a significant post-operative bleeding risk. Investigators classified procedures as “emergent” or “non-emergent.” RESULTS: 11,417 procedures occurred in 6162 patients: 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 procedures (35.8%), study drug was not stopped. Median time of study drug stop was 4 days peri-procedure for both treatment groups. A second "bridging" anticoagulant, most commonly low-molecular-weight heparin, was used in 1335 procedures (11.7%). Of 5660 events with apixaban, SEE and major bleeding were associated with 0.43% and 1.55%, respectively; of 5757 events with warfarin, corresponding values were 0.56% and 1.80%, respectively. CONCLUSIONS: Procedures are common in patients with atrial fibrillation. The majority of procedures are non-major and non-emergent, and anticoagulation therapy is likely to be stopped peri-procedure. Overall and among emergent procedures, rates of clinical events in the first 30 days post-procedure were low and comparable between treatment groups. Study Supported by: Bristol-Myers Squibb Company and Pfizer Inc. Editorial assistance (i.e., formatting the abstract to ensure compliance with AAN guidelines) was provided by Claire Hall of Caudex Medical and was funded by Bristol-Myers Squibb Company and Pfizer Inc.
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 | 0.002 | 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 | 0.000 |
| 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".