Anticoagulation Prescription and Outcomes in Relation to Renal Function in Patients with Atrial Fibrillation: Results from GLORIA-AF
Bibliographic record
Abstract
Abstract Objective Anticoagulation management in patients with atrial fibrillation (AF) and impaired renal function is challenging. This study aimed to evaluate anticoagulation prescription patterns in relation to renal function and to describe 2-year clinical outcomes among dabigatran users. Methods Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation (GLORIA-AF) is an international, prospective, and observational study program involving patients with newly diagnosed AF at risk for stroke. Prescription patterns were assessed by creatinine clearance (CrCl) at enrollment. Dabigatran users were followed for 2 years. Clinical outcomes were standardized for stroke and bleeding risk, based on CHA2DS2-VASc and HAS-BLED scores, with missing values imputed. Results Baseline CrCl values were available for 12,056 of 15,308 eligible patients (79%). With declining renal function, prescriptions increased for vitamin K antagonists (VKAs) and decreased for dabigatran (30–47% and 34–12%, respectively). The prescription of other non-vitamin K antagonists remained similar across CrCl groups (14–19%). In 4,873 dabigatran users, standardized stroke rates were low across all CrCl groups; 0.58/100 patient-years (95% confidence interval [CI]: 0.30–0.90) in CrCl ≥80 mL/min, 0.85 (95% CI: 0.48–1.21) in CrCl 50 to 79 mL/min, and 0.33 (95% CI: 0.06–1.11) in CrCl 30 to 49 mL/min. Similarly, major bleeding rates were low and numerically increased with declining renal function (0.68/100 patient-years, 95% CI: 0.39–1.03; 0.92, 95% CI: 0.58–1.32; and 1.26, 95% CI: 0.66–1.97, respectively). Conclusion In patients with AF, VKA prescriptions increased and dabigatran prescriptions decreased with declining renal function. Rates of stroke and major bleeding in dabigatran patients remained low across the categories of renal impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".