The effects of apixaban on hospitalizations in patients with different types of atrial fibrillation: insights from the AVERROES trial
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate the effects of apixaban, a novel oral factor Xa inhibitor, on the need for cardiovascular hospitalization. METHODS AND RESULTS: Our analysis is based on data from AVERROES, a randomized double-blind trial testing the efficacy and safety of apixaban against aspirin for the prevention of thrombo-embolism in 5599 atrial fibrillation (AF) patients unsuitable for vitamin K antagonist therapy. Hospitalizations were captured by dedicated case report forms and the outcome variable was time from randomization to the first hospitalization. Effects of treatment with apixaban on the rates of cardiovascular and non-cardiovascular hospitalizations were assessed using Cox proportional hazards regression models. During a mean follow-up of 1.1 years, 800 patients were hospitalized at least once for cardiovascular reasons, 442 (15.4%/year) in the aspirin group, 358 (12.3%) in the apixaban group [hazard ratio (HR) 0.80, 95% confidence interval (CI) 0.69-0.92; P = 0.002]. The reduction in cardiovascular hospitalization in the apixaban arm was predominantly due to a reduction in hospitalization for strokes, but there were also fewer hospitalizations for other cardiovascular causes. Patients with paroxysmal AF were significantly more likely to be hospitalized for AF treatment, whereas more heart failure admissions occurred in patients with permanent AF. Assignment to apixaban was the only independent predictor for a reduction in hospitalization. Cardiovascular hospitalization was the strongest independent predictor of subsequent mortality (HR: 3.95, 95% CI: 3.06-5.09; P < 0.001). CONCLUSION: In AVERROES, patients on apixaban therapy were less likely to be hospitalized. This may have important consequences for patients' well-being and for healthcare resources. This trial is registered underClinicalTrials.gov number, NCT00496769.
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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 | 0.006 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".