Restart of Anticoagulant Therapy and Risk of Thrombosis, Rebleeding, and Death after Factor Xa Inhibitor Reversal in Major Bleeding Patients
Bibliographic record
Abstract
Abstract Background Lack of data on balancing bleeding and thrombosis risk causes uncertainty about restarting anticoagulants after major bleeding. Anticoagulant reversal trials offer prospectively gathered data after major bleeding with well-documented safety events and restarting behavior. Objectives To examine the relationship of restarting anticoagulation with thrombosis, rebleeding, and death. Methods This is a posthoc analysis of a prospective factor Xa inhibitor reversal study at 63 centers in North America and Europe. We compared outcomes of restarted patients with those not restarted using landmark and time-dependent Cox proportional hazards models. Outcomes included thrombotic and bleeding events and death and a composite of all three. Results Of 352 patients enrolled, oral anticoagulation was restarted in 100 (28%) during 30-day follow-up. Thirty-four (9.7%) had thrombotic events, 15 (4.3%) had bleeding events (after day 3), and 49 (14%) died. In the landmark analysis comparing patients restarted within 14 days to those not, restarting was associated with decreased thrombotic events (hazard ratio [HR] = 0.112; 95% confidence interval [CI]: 0.001–0.944; p = 0.043) and increased rebleeding (HR = 8.39; 95% CI: 1.13–62.29; p = 0.037). The time-dependent Cox model showed evidence for a reduction in a composite (thrombotic events, bleeding, and death) attempting to capture net benefit (HR = 0.384; 95% CI: 0.161–0.915; p = 0.031). Conclusion This analysis provides modest evidence that restarting anticoagulation in factor Xa inhibitor-associated major bleeding patients is correlated with reduced risk of thrombotic events and increased risk of rebleeding. There is low-level evidence of net benefit for restarting. A randomized trial of restarting would be appropriate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".