Comparative Effectiveness and Safety of Oral Anticoagulants Across Kidney Function in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background: Patients with atrial fibrillation and severely decreased kidney function were excluded from the pivotal non–vitamin K antagonist oral anticoagulants (NOAC) trials, thereby raising questions about comparative safety and effectiveness in patients with reduced kidney function. The study aimed to compare oral anticoagulants across the range of kidney function in patients with atrial fibrillation. Methods and Results: Using a US administrative claims database with linked laboratory data, 34 569 new users of oral anticoagulants with atrial fibrillation and estimated glomerular filtration rate ≥15 mL/(min·1.73 m 2 ) were identified between October 1, 2010 to November 29, 2017. The proportion of patients using NOACs declined with decreasing kidney function—73.5%, 69.6%, 65.4%, 59.5%, and 45.0% of the patients were prescribed a NOAC in estimated glomerular filtration rate ≥90, 60 to 90, 45 to 60, 30 to 45, 15 to 30 mL/min per 1.73 m 2 groups, respectively. Stabilized inverse probability of treatment weighting was used to balance 4 treatment groups (apixaban, dabigatran, rivaroxaban, and warfarin) on 66 baseline characteristics. In comparison to warfarin, apixaban was associated with a lower risk of stroke (hazard ratio [HR], 0.57 [0.43–0.75]; P <0.001), major bleeding (HR, 0.51 [0.44–0.61]; P <0.001), and mortality (HR, 0.68 [0.56–0.83]; P <0.001); dabigatran was associated with a similar risk of stroke but a lower risk of major bleeding (HR, 0.57 [0.43–0.75]; P <0.001) and mortality (HR, 0.68 [0.48-0.98]; P =0.04); rivaroxaban was associated with a lower risk of stroke (HR, 0.69 [0.51–0.94]; P =0.02), major bleeding (HR, 0.84 [0.72–0.99]; P =0.04), and mortality (HR, 0.73 [0.58–0.91]; P =0.006). There was no significant interaction between treatment and estimated glomerular filtration rate categories for any outcome. When comparing one NOAC to another NOAC, there was no significant difference in mortality, but some differences existed for stroke or major bleeding. No relationship between treatments and falsification end points was found, suggesting no evidence for substantial residual confounding. Conclusions: Relative to warfarin, NOACs are used less frequently as kidney function declines. However, NOACs appears to have similar or better comparative effectiveness and safety across the range of kidney function.
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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 | 0.017 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".