Impact of Modifiable Bleeding Risk Factors on Major Bleeding in Patients With Atrial Fibrillation Anticoagulated With Rivaroxaban
Bibliographic record
Abstract
Background Reducing major bleeding events is a challenge when managing anticoagulation in patients with atrial fibrillation. This study evaluated the impact of modifiable and nonmodifiable bleeding risk factors in patients with atrial fibrillation receiving rivaroxaban and estimated the impact of risk factor modification on major bleeding events. Methods and Results Modifiable and nonmodifiable risk factors associated with major bleeding events were identified from the XANTUS (Xarelto for Prevention of Stroke in Patients With Atrial Fibrillation) prospective registry data set (6784 rivaroxaban-treated patients). Parameters showing univariate association with bleeding were used to construct a multivariable model identifying independent risk factors. Modeling was used to estimate attributed weights to risk factors. Heavy alcohol use (hazard ratio [HR]=2.37; 95% CI 1.24-4.53); uncontrolled hypertension (HR after parameter-wise shrinkage=1.79; 95% CI 1.05-3.05); and concomitant treatment with antiplatelets, nonsteroidal anti-inflammatory drugs, or paracetamol (HR=1.80; 95% CI 1.24-2.61) were identified as modifiable, independent bleeding risk factors. Increasing age (HR=1.25 [per 5-year increment]; 95% CI 1.12-1.38); heart failure (HR=1.97; 95% CI 1.36-2.86); and vascular disease (HR=1.91; 95% CI 1.32-2.77) were identified as nonmodifiable bleeding risk factors. Overall, 128 (1.9%) patients experienced major bleeding events; of these, 11% had no identified bleeding risk factors, 50% had nonmodifiable bleeding risk factors only, and 39% had modifiable bleeding risk factors (with or without nonmodifiable risk factors). The presence of 1 modifiable bleeding risk factor doubled the risk of major bleeding. Conclusions Elimination of modifiable bleeding risk factors is a potentially effective strategy to reduce bleeding risk in atrial fibrillation patients receiving rivaroxaban. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01606995.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".