Abstract 12862: Predictors of Hospitalization and Effects of Dabigatran and Warfarin on Hospitalization Rates: An Analysis From The Re-ly Trial
Bibliographic record
Abstract
Introduction: Hospitalization is common in patients with atrial fibrillation (AF), and predicts poor prognosis, but is poorly understood. Aim: We evaluated predictors of hospitalization in patients with AF treated with dabigatran etexilate (DE) or warfarin in RE-LY to understand predictors of hospitalization and to further evaluate the effects of anticoagulant choices on hospitalization. Methods: A multivariate regression model was developed to determine predictors of hospitalization. Hospitalization due to bleeding events were identified and rates were compared between DE and warfarin. Results: Of 18,113 patients in RE-LY, 7,200 (39.8%) were hospitalized at least once during mean follow up of 2 years, with 14,025 total hospitalizations. Compared to patients not hospitalized, patients with hospitalizations were older, more often had coronary artery disease, prior MI, a history of heart failure, moderate renal impairment, hypertension, diabetes and higher CHADS 2 scores. Multivariate analysis identified peripheral artery disease (PAD; HR 1.44, 95% CI 1.22-1.69), a CHADS 2 score of ≥ 3 (HR 1.29, CI 1.11-1.51), VKA-naïve (HR 1.28, CI 1.19-1.37), left ventricular ejection fraction (EF) ≤ 40% (HR 1.24, CI 1.12-1.36), and renal impairment with CrCL ≤ 50mL/min (HR 1.12, CI 1.02-1.23) as predictors of hospitalization. First hospitalization rates were 39.5%, 41.6% and 42.6% for DE 110 mg bid, DE 150 mg bid and warfarin, respectively. The treatment effects of both DE doses compared to warfarin were consistent, between patients hospitalized and those not. Hospitalization occurred less often with DE 110 compared to warfarin (RR = 0.93, CI 0.87-0.99) and compared to DE 150 (RR=0.95, CI 0.89-1.01). Hospitalizations for bleeding events occurred less often with DE 110 (3.5%) compared both to warfarin (4.6%) (p=0.02) and to DE 150 (5.2%) (p<0.001); rates were statistically similar between DE 150 and warfarin (p=0.07). Conclusion: Predictors of hospitalization included common comorbidities of an elderly AF population, such as PAD, higher CHADS 2 scores, low EF and renal impairment. DE 110 mg bid was associated with significantly fewer hospitalizations compared to both warfarin and DE 150 bid; in part due to reduction in hospitalizations for bleeding events.
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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 | 0.004 |
| 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) | 0.003 | 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".