Safety and Effectiveness of Apixaban versus Warfarin by Kidney Function in Atrial Fibrillation
Bibliographic record
Abstract
Key Points This real-world study involved a large cohort of 38,598 adults with atrial fibrillation from five jurisdictions across Australia and Canada. This study supports the use of apixaban as a safe and effective alternative to warfarin for atrial fibrillation across differing levels of kidney function. This study also adds important safety data on the use of apixaban in patients with reduced kidney function. Background Evidence to guide the use of apixaban in people with atrial fibrillation (AF) and CKD in routine clinical practice has been limited. We assessed comparative safety (major bleeding) and effectiveness (ischemic stroke and death) of apixaban versus warfarin in patients with AF across the spectrum of non–dialysis-dependent CKD using large, routinely collected data. Methods We combined findings from five retrospective cohorts (2013–2018) across Australia and Canada. Adults with AF, new dispensation of apixaban or warfarin, and a recorded eGFR grouped as ≥60, 45–59, 30–44, and <30 ml/min per 1.73 m 2 were included. Patients on dialysis or kidney transplant recipients were excluded. We assessed outcomes within 1 year of initiating either therapy: ( 1 ) composite of all-cause death, ischemic stroke, or transient ischemic attack and ( 2 ) first hospitalization for major bleeding (intracranial, gastrointestinal, or other). Cox models estimated hazard ratios (HRs; 95% confidence intervals) for outcomes across eGFR categories, after 1:1 matching using propensity scores. We summarized center-level data using random effects meta-analysis. Results Among 38,598 matched apixaban and warfarin users, there were 4130 (10.7%) ischemic and 697 (1.8%) bleeding events within 1 year. Apixaban was associated with lower or similar risk for the ischemic outcome compared with warfarin in all eGFR categories (pooled HRs [95% confidence interval]: 0.78 [0.64 to 0.94], 0.77 [0.62 to 0.97], 0.82 [0.68 to 0.98], and 0.99 [0.68 to 1.45] for eGFR ≥60, 45–59, 30–44, and <30 ml/min per 1.73 m 2 , respectively). Apixaban was associated with lower or similar risk of bleeding across the range of kidney function (pooled HRs: 0.55 [0.43 to 0.69], 0.73 [0.52 to 1.02], 0.55 [0.31 to 0.97], and 0.68 [0.47 to 0.99], respectively). There was no significant heterogeneity across jurisdictions or eGFR categories. Conclusions In adults with AF and non–dialysis-dependent CKD, apixaban compared with warfarin was associated with lower or similar risk of ischemic and bleeding outcomes. Our results suggest that apixaban offers a favorable risk-benefit ratio in patients with AF independent of kidney function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".