Predictors of Bleeding in the Perioperative Anticoagulant Use for Surgery Evaluation Study
Bibliographic record
Abstract
Background In the PAUSE (Perioperative Anticoagulant Use for Surgery Evaluation) Study, a simple, standardized, perioperative interruption strategy was provided for patients with nonvalvular atrial fibrillation taking direct oral anticoagulants (DOACs). Our objective was to define the factors associated with perioperative bleeding. Methods and Results We analyzed bleeding as the composite of major and clinically relevant nonmajor bleeding. Putative predictors of bleeding, and preoperative DOAC level were prospectively collected during recruitment. We used stratified logistic regression models for analysis. All statistical analyses were performed in R version 3.6.0. There were 3007 patients requiring perioperative DOAC interruption. More than one third of the included patients underwent a high bleeding risk procedure. The 30‐day rates of major and clinically relevant nonmajor bleeding were 3.02% in apixaban (n=1257), 2.84% in dabigatran (n=668), and 4.16% for rivaroxaban (n=1082). Multivariate analysis stratified by region found more bleeding for hypertension (odds ratio [OR], 1.79; 95% CI 1.07‐2.99; P =0.027), and prior bleeding (OR, 1.71; 95% CI, 1.08‐2.71; P =0.021). Surgical bleed risk classification (high‐ versus low‐risk) as a predictor of bleeding was only significant in the univariate analysis. The prediction model for major and clinically relevant nonmajor bleeding had an area under the curve of 0.71, and the preoperative DOAC level did not improve the area under the curve of the model. Conclusions In patients treated with DOACs who required an elective surgery/procedure and were managed with standardized DOAC interruption and resumption, there we did not find reversible risk factors for bleeding, suggesting that adjustment of the PAUSE management protocol to mitigate against bleeding is not needed.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".