Abstract 154: Adherence to Rivaroxaban Compared to Other Oral Anticoagulant Agents Among Patients With Non-Valvular Atrial Fibrillation
Bibliographic record
Abstract
Background: Adherence to oral anticoagulant (OAC) agents is important for patients with non-valvular atrial fibrillation (NVAF) to prevent potentially severe adverse events. Objectives: To compare real-world adherence rates for rivaroxaban vs other oral anticoagulant agents (apixaban, dabigatran, and warfarin) among patients with NVAF using claims-based data. Methods: Healthcare claims from the IMS Health Real-World Data Adjudicated Claims database (01/2011-06/2015) were used to assess 6 month adherence rates defined as the percentage of patients with proportion of days covered (PDC) ≥0.8 and ≥0.9. Patients were included if they had ≥2 dispensings of rivaroxaban, apixaban, dabigatran, or warfarin at least 180 days apart (the first was termed as the index date), had > 60 days of supply, had ≥ 6 months of pre-index eligibility, had ≥ 1 AF diagnosis pre-index or at index date, and without valvular involvement. A logistic regression model was used to evaluate adherence to therapy adjusting for sociodemographic and clinical characteristics, insurance type, index month and year, previous OAC use, and mental-health risk factors for non-adherence (e.g., mental disorders, bipolar). Results: A total of 13,645 rivaroxaban, 6,304 apixaban, 3,360 dabigatran, and 13,366 warfarin patients were identified. A significantly higher proportion of rivaroxaban users were adherent to therapy (PDC ≥ 0.8 at 6 months vs apixaban, dabigatran, and warfarin users; Table). After adjustment, the proportion of patients adherent to therapy remained significantly higher for rivaroxaban users vs apixaban (absolute difference [AD]: 5.8%), dabigatran (AD: 9.5%), and warfarin users (AD: 13.6%; all P<0.001; Table). More pronounced differences were found with a PDC ≥0.9 (Table). Conclusion: Among NVAF patients, rivaroxaban was associated with significantly higher adherence rates relative to other OACs, whether using a PDC of ≥0.8 or ≥0.9, which could translate into improved patient outcomes and lower healthcare costs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".