Peri-operative Adverse Outcomes in Patients with Atrial Fibrillation Taking Warfarin or Edoxaban: Analysis of the ENGAGE AF-TIMI 48 Trial
Bibliographic record
Abstract
Background Peri-operative management of anticoagulated patients with atrial fibrillation (AF) is challenging. To gain information on the peri-operative management of edoxaban, we compared outcomes in patients on warfarin or edoxaban enrolled in ENGAGE AF-TIMI 48 who underwent a surgery or invasive procedure. Methods Data from patients undergoing their first surgery/procedure were analysed and results compared by anticoagulant (warfarin vs. higher- or lower-dose edoxaban regimen [HDER and LDER, respectively]). Patients were classified by procedural management: anticoagulant interrupted (last dose 4–10 days pre-procedure) or anticoagulant continued (last dose ≤ 3 days pre-procedure). Stroke/systemic embolism (SSE), major bleeding (MB), MB or clinically relevant non-MB (CRNMB) and death were assessed from 7 days pre- until 30 days post-procedure. The chi-square test was used to compare outcomes across treatment groups. Results A total of 7,193 patients (34%) underwent surgery/procedure: 3,116 had anticoagulant interrupted, 4,077 had anticoagulant continued. Among patients on warfarin, HDER and LDER who had anticoagulant interrupted, rates of SSE were 0.6, 0.5 and 0.9% (p = 0.53), rates of MB were 1.0, 1.2 and 1.1% (p = 0.94) and rates of MB or CRNMB were 3.9, 4.2 and 3.6% (p = 0.78); among patients on warfarin, HDER and LDER who had anticoagulant continued, rates of SSE were 1.1, 0.7 and 0.9% (p = 0.51), rates of MB were 3.6, 2.6 and 2.4% (p = 0.13) and rates of MB or CRNMB were 8.5, 7.9 and 6.6% (p = 0.17). Conclusion In patients requiring surgery/procedure in ENGAGE AF-TIMI 48, peri-operative rates of SSE, MB and death were not significantly different in patients who received edoxaban or warfarin.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".