Persistence With Dabigatran Therapy at 2 Years in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend long-term oral anticoagulation therapy for stroke prevention in patients with atrial fibrillation (AF). Treatment discontinuation rates in vitamin K antagonist (VKA)-treated patients are high but may be lower with non-VKA oral anticoagulant agents. OBJECTIVES: The goal of this study was to describe and explore predictors of dabigatran etexilate persistence in patients with newly diagnosed AF over 2 years of follow-up. METHODS: Consecutive patients newly diagnosed with AF and ≥1 stroke risk factor were followed up for 2 years. Dabigatran nonpersistence was defined as discontinuation of dabigatran for >30 days. A multivariable Cox regression model included region as well as patient clinical and sociodemographic characteristics to explore predictors of nonpersistence. RESULTS: Eligible patients (N = 2,932) took ≥1 dabigatran dose; their mean age was 70.3 ± 10.2 years, and 55.3% were male. The 2-year probability of dabigatran persistence was 69.2%. Approximately 7% switched to a factor Xa inhibitor and 6% to a VKA. Approximately one-third of dabigatran discontinuations were primarily due to serious or nonserious adverse events. Patients from North America had the highest discontinuation risk, and Latin America had the lowest. Minimally symptomatic or asymptomatic AF and permanent AF were associated with a lower risk for dabigatran nonpersistence. Previous proton pump inhibitor use was associated with a higher risk for dabigatran nonpersistence. CONCLUSIONS: Probability of treatment persistence with dabigatran after 2 years was approximately 70%. Nearly one-half of the patients who stopped dabigatran switched to another oral anticoagulant agent. Patients from North America, and those with paroxysmal, persistent, or symptomatic AF, may be at a higher risk for discontinuing dabigatran.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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".