Impact of Switching From a Vitamin K Antagonist to Rivaroxaban on Satisfaction With Anticoagulation Therapy: The XANTUS‐ACTS Substudy
Bibliographic record
Abstract
BACKGROUND: The efficacy, safety, and ease of use of rivaroxaban may reduce anticoagulation-treatment burden and improve nonvalvular atrial fibrillation (NVAF) patient satisfaction compared with vitamin K antagonists (VKAs). HYPOTHESIS: Transitioning from a VKA to rivaroxaban improves treatment satisfaction in routine practice. METHODS: Xarelto for Prevention of Stroke in Patients With Atrial Fibrillation (XANTUS) is a prospective, noninterventional study in patients with NVAF prescribed rivaroxaban for prevention of stroke in routine practice. Patients receiving a VKA 4 weeks prior to the initial XANTUS study visit and switched to rivaroxaban were asked to complete the Anti-Clot Treatment Scale (ACTS). Changes from the initial visit to the first follow-up visit at ∼ 3 months (corresponding to a comparison of rivaroxaban vs prior VKA) for ACTS burden and benefit scores were calculated using and reported as least squared mean differences (LSMDs) with 95% confidence intervals (CIs). RESULTS: The study included 1291 NVAF patients with prior VKA treatment. The mean baseline ACTS burden and benefit scores were 50.51 ± 8.42 and 10.30 ± 2.70, respectively. After ∼ 3 months of rivaroxaban treatment, LSMDs were 4.38 points (95% CI: 2.53-6.22, P < 0.0001) for the burden and 1.01 points (95% CI: 0.27-1.75, P = 0.0075) for the benefit score. Fifty-four percent and 48% of patients reported experiencing at least a minimally important clinical difference in burden and benefit scores, respectively. CONCLUSIONS: Within this XANTUS cohort, switching from a VKA to rivaroxaban yielded statistically and clinically significant improvements in ACT burden and benefit scores.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".