Effectiveness and Safety of Apixaban versus Rivaroxaban in Patients with Atrial Fibrillation and Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Abstract Aims To evaluate the effectiveness and safety of apixaban versus rivaroxaban among patients with nonvalvular atrial fibrillation (NVAF) and type 2 diabetes mellitus (T2DM). Methods and Results Using the United Kingdom's Clinical Practice Research Datalink linked to the Hospital Episode Statistics repository, and the Office for National Statistics database, we identified a cohort of patients with NVAF and T2DM newly treated with apixaban or rivaroxaban between 2013 and 2020. Propensity scores with standardized mortality ratio weighting were used to control for confounding. We used weighted Cox proportional hazards models to estimate separately the hazard ratios (HRs) with 95% confidence intervals (CIs) of ischemic stroke, major bleeding, and major adverse limb events associated with the use of apixaban compared with rivaroxaban. We also evaluated whether the risk was modified by age, sex, duration of diabetes, microvascular and macrovascular complications of diabetes, nephropathy, CHA2DS2-VASc and HAS-BLED scores, and by dose (standard vs. low dose). Results The cohort included 11,561 apixaban and 8,265 rivaroxaban users. Apixaban was associated with a similar risk of stroke (HR: 0.99, 95% CI: 0.79–1.23), and a 32% reduced risk of major bleeding (HR: 0.68, 95% CI: 0.59–0.78), compared with rivaroxaban. The risk of major adverse limb events was similar between apixaban and rivaroxaban (HR: 0.75, 95% CI: 0.54–1.04). Overall, the risk of ischemic stroke and major bleeding was consistent in stratified analyses. Conclusion Among patients with NVAF and T2DM, apixaban was associated with a similar risk of stroke and a lower risk of major bleeding compared with rivaroxaban.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".