P2887Two-year outcomes of dabigatran etexilate treatment in patients with co-morbid heart failure and atrial fibrillation: the GLORIA-AF registry
Bibliographic record
Abstract
Background: Heart failure (HF) and atrial fibrillation (AF) are common cardiovascular conditions that are independently associated with poor outcomes but often occur concurrently. Co-diagnosis confers increased risks of thromboembolic events that are more than additive. Objectives: To describe baseline characteristics and 2-year outcomes in AF patients with and without HF who were treated with dabigatran etexilate (DE) in a prospective, global registry program (GLORIA-AF). Methods: Newly diagnosed non-valvular AF (NVAF) patients with a CHA2DS2-VASc score ≥1 were consecutively enrolled. Baseline characteristics of the total group and clinical outcomes in patients prescribed DE were assessed according to presence or absence of HF at time of enrollment. Results: A total of 15,308 patients were enrolled, including 3679 (24.0%) with HF, 11,475 (75.0%) without HF, and 154 (1.0%) with HF status unknown. Patients with HF were more often male (60.9%), and had higher rates of prior myocardial infarction (18.8% vs. 7.8%), coronary artery disease (31.6% vs. 16.7%), and greater proportions of symptomatic (39.5% vs. 24.6%) and permanent AF (15.2% vs. 9.7%) compared to patients without HF. Of 4873 patients prescribed DE, 1169 (24.0%) patients had prior HF, 3658 (75.1%) no prior HF, and 46 patients (0.9%) uncertain. Stroke risk was high (CHA2DS2-VASc score ≥2) in 94.3% with HF and 85.8% of patients without HF, while 6% of patients with HF versus 7% without HF had known high risk of bleed (HAS-BLED ≥3). Clinical characteristics of the patients prescribed DE showed differences for the same variables noted in the overall group between patients with and without HF. Crude incidence rates for outcomes are presented in the table. Incidence rates standardized by stroke and bleeding risk will be presented.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".