Characteristics and 2-Year Outcomes of Dabigatran Treatment in Patients with Heart Failure and Atrial Fibrillation: GLORIA-AF
Bibliographic record
Abstract
Abstract Aims This study aimed to describe baseline characteristics of patients with atrial fibrillation (AF) at risk of stroke with and without history of heart failure (HF) and report 2-year outcomes in the dabigatran-treated subset of a prospective, global, observational study (GLORIA-AF). Methods and results Newly diagnosed patients with AF and CHA2DS2-VASc score ≥ 1 were consecutively enrolled. Baseline characteristics were assessed by the presence or absence of HF diagnosis at enrolment. Incidence rates for outcomes in dabigatran-treated patients were estimated with and without standardization by stroke (excluding HF component) and bleeding risk scores. A total of 15 308 eligible patients were enrolled, including 15 154 with known HF status; of these, 3679 (24.0%) had been diagnosed with HF, 11 475 (75.0%) had not. Among 4873 dabigatran-treated patients, 1169 (24.0%) had HF, and 3658 (75.1%) did not; the risk of stroke was high (CHA2DS2-VASc score ≥ 2) for 94.3% of patients with HF and 85.8% without, while 6.0% and 7.0%, respectively, had a high bleeding risk (HAS-BLED ≥ 3). Incidence rates of all-cause death in dabigatran-treated patients with and without HF, standardized for CHA2DS2-VASc and HAS-BLED scores, were 4.76 vs. 1.80 per 100 patient years (py), with roughly comparable rates of stroke (0.82 vs. 0.60 per 100 py) and major bleeding (1.20 vs. 0.92 per 100 py). Conclusions Patients with AF and history of HF may have greater disease burden at AF diagnosis and increased mortality rates vs. patients without HF. Stroke and major bleeding rates were roughly comparable between groups confirming the long-term safety and effectiveness of dabigatran in patients with HF.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".