Reasons for hospitalization and risk of mortality in patients with atrial fibrillation treated with dabigatran or warfarin in the Randomized Evaluation of Long-term Anticoagulation Therapy (RE-LY) trial
Bibliographic record
Abstract
AIMS: Hospitalizations are common among patients with atrial fibrillation. This article aimed to analyse the causes and consequences of hospitalizations occurring during the Randomized Evaluation of Long-term Anticoagulation Therapy (RE-LY) trial. METHODS AND RESULTS: The RE-LY database was used to evaluate predictors of hospitalization using multivariate regression modelling. The relationship between hospitalization and subsequent major adverse cardiac events was evaluated in a time dependent Cox proportional-hazard modelling. Of the 18 113 patients in RE-LY, 7200 (39.8%) were hospitalized at least once during a mean follow-up of 2 years. First hospitalization rates were 2312 (39.5%) for dabigatran etexilate (DE) 110, 2430 (41.6%) for DE 150, and 42.6% (N = 2458) for warfarin. Hospitalization was associated with post-discharge death [absolute event rate 9.1% vs. 2.2%; adjusted hazard ratio (HR) 3.6, 95% confidence interval (CI) 3.2-4.0, P < 0.0001], vascular death (adjusted HR 2.9, 95% CI 2.5-3.3, P < 0.0001), and sudden cardiac death (adjusted HR 2.3; 95% CI 1.8-2.9, P < 0.0001). Cardiovascular hospitalization was also associated with an increased risk of post-discharge death (adjusted HR 2.8, 95% CI 2.5-3.2, P < 0.0001), vascular death (adjusted HR 2.8, 95% CI 2.4-3.2, P < 0.0001), and sudden cardiac death (adjusted HR 2.1, 95% CI 1.6-2.7, P < 0.0001) compared with patients not hospitalized for any cardiovascular reason. CONCLUSION: Hospitalizations are associated an increased risk of with death and cardiovascular death in patients with atrial fibrillation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".