Dabigatran Persistence and Outcomes Following Discontinuation in Atrial Fibrillation Patients from the GLORIA-AF Registry
Bibliographic record
Abstract
Prospective studies evaluating persistence to nonvitamin K antagonist oral anticoagulants in patients with atrial fibrillation are needed to improve our understanding of drug discontinuation. The study objective was to evaluate if and when patients with newly diagnosed atrial fibrillation stop dabigatran treatment and to report outcomes following discontinuation. Patients prescribed dabigatran in diverse clinical practice settings were consecutively enrolled and followed for 2 years. Dabigatran persistence over time, reasons for discontinuation, and outcomes post discontinuation were assessed. Of 4,859 patients, aged 70.2 ± 10.4 years, 55.7% were male. Overall 2-year dabigatran persistence was 70.9% (95% confidence interval [CI] 69.6 to 72.2). Persistence probability was lower in the first 6-month period (83.7% [82.7 to 84.8]) than in subsequent periods for patients on dabigatran at the start of each period (6 to 12 months, 92.5% [91.6 to 93.3]; 12 to 18 months, 95.1% [94.3 to 95.8]; 18 to 24 months, 96.3% [95.6 to 96.9]). Of 1,305 patients (26.9%) who discontinued dabigatran, adverse events were reported as the reason for discontinuation in 457 (35.0%). Standardized stroke incidence rate post discontinuation (per 100 patient-years) in patients discontinuing without switching to another oral anticoagulant was 1.76 (95% CI 0.89 to 2.76) and 1.02 (95% CI 0.43 to 1.76) in those who switched, consistent with the expected benefit of remaining on treatment. Patients persistent with treatment at 1 year had >90% probability of remaining persistent at 2 years suggesting clinical interventions to improve persistence should be focused on the early period following treatment initiation.
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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 | 0.002 | 0.004 |
| 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".