Perioperative bridging anticoagulation during dabigatran or warfarin interruption among patients who had an elective surgery or procedure
Bibliographic record
Abstract
In patients with atrial fibrillation (AF) who require interruption of dabigatran or warfarin for an elective surgery/procedure, the risks and benefits of perioperative bridging anticoagulation is uncertain.We accessed the database from RE-LY, a randomised trial comparing dabigatran with warfarin for stroke prevention in AF, to assess the potential benefits and risks of bridging. In patients who had a first interruption of dabigatran or warfarin for an elective surgery/procedure, we compared the risk for major bleeding (MB), stroke or systemic embolism (SSE) and any thromboembolism (TE) in patients who were bridged or not bridged during the period of seven days before until 30 days after surgery/procedure. We used multivariable Cox regression to adjust for potential confounders.Bridging was used more during warfarin interruption than dabigatran interruption (27.5 % vs 15.4 %; p< 0.001). With dabigatran interruption, bridged patients had more MB (6.5 % vs 1.8 %, p< 0.001) than those not bridged but bridged and not bridged groups did not differ for any TE (1.2 % vs 0.6 %, p=0.16) and SSE (0.5 % vs 0.3 %, p=0.46). With warfarin interruption, bridged patients had more MB (6.8 % vs 1.6 %, p< 0.001) and any TE (1.8 % vs 0.3 %, p=0.007) than those not bridged but bridged and not bridged groups did not differ for SSE (0.5 % vs 0.2 %, p=0.321). In conclusion, in patients who interrupted dabigatran or warfarin for a surgery/ procedure in the RE-LY trial, use of bridging anticoagulation appeared to increase the risk for major bleeding irrespective of dabigatran or warfarin interruption.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".