Management and clinical outcomes in patients treated with apixaban vs warfarin undergoing procedures
Bibliographic record
Abstract
Using data from ARISTOTLE, we describe the periprocedural management of anticoagulation and rates of subsequent clinical outcomes among patients chronically anticoagulated with warfarin or apixaban. We recorded whether (and for how long) anticoagulant therapy was interrupted preprocedure, whether bridging therapy was used, and the proportion of patients who experienced important clinical outcomes during the 30 days postprocedure. Of 10 674 procedures performed during follow-up in 5924 patients, 9260 were included in this analysis. Anticoagulant treatment was not interrupted preprocedure 37.5% of the time. During the 30 days postprocedure, stroke or systemic embolism occurred after 16/4624 (0.35%) procedures among apixaban-treated patients and 26/4530 (0.57%) procedures among warfarin-treated patients (odds ratio [OR] 0.601; 95% confidence interval [CI] 0.322-1.120). Major bleeding occurred in 74/4560 (1.62%) procedures in the apixaban arm and 86/4454 (1.93%) in the warfarin arm (OR 0.846; 95% CI 0.614-1.166). The risk of death was similar with apixaban (54/4624 [1.17%]) and warfarin (49/4530 [1.08%]) (OR 1.082; 95% CI 0.733-1.598). Among patients in ARISTOTLE, the 30-day postprocedure stroke, death, and major bleeding rates were low and similar in apixaban- and warfarin-treated patients, regardless of whether anticoagulation was stopped beforehand. Our findings suggest that many patients on chronic anticoagulation can safely undergo procedures; some will not require a preprocedure interruption of anticoagulation. ARISTOTLE was registered at www.clinicaltrials.gov as #NCT00412984.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".