Abstract 152: Adherence to Rivaroxaban versus Apixaban Among Patients With Atrial Fibrillation: Analysis of Overall Population and Subgroups of Prior Oral Anticoagulant Users
Bibliographic record
Abstract
Background: Medication nonadherence predicts poor outcomes among patients with non-valvular atrial fibrillation (NVAF). Understanding differences in adherence rates among non-vitamin K oral anticoagulants (NOACs) could guide treatment decisions, promote adherence and improve clinical outcomes. Objective: To compare adherence to rivaroxaban and apixaban among the overall NVAF population and subgroups at higher risk for adverse clinical outcomes (e.g., prior OAC use, multiple comorbidities, and with risk factors for nonadherence). Methods: Using healthcare claims from the Truven Health Analytics MarketScan database from 7/2012-7/2015, adult patients with 2 dispensings of rivaroxaban or apixaban at least 180 days apart, with > 60 days of supply, ≥ 6 months of pre- and post-index eligibility, ≥ 2 atrial fibrillation diagnoses pre- or post-index, and without valvular involvement were identified. Propensity score methods were used to create matched cohorts of rivaroxaban and apixaban patients, adjusting for demographics, risk factors for nonadherence (e.g., bipolar, anxiety), previous OAC use, and clinical characteristics. Adherence was assessed using the percentage of patients with proportion of days covered ≥0.8 at 6 months. Subgroups of patients with prior OAC use, prior OAC use and a Quan-Charlson Comorbidity index ≥2, and prior OAC use with/without nonadherence risk factors were evaluated. Results: 14,635 NVAF subjects were included in each of 2 matched cohorts. All baseline characteristics were balanced between cohorts. At 6 months, significantly more rivaroxaban users were adherent to treatment as compared to apixaban users (82.4% vs 78.5%; absolute difference of 3.9%; P<0.01). Rivaroxaban users had significantly higher adherence rates in all subgroups examined with prior OAC agents (Figure). Conclusion: Rivaroxaban users had consistently higher adherence rates than apixaban users overall and among all NVAF subgroups examined.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".