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Record W4409284021 · doi:10.34067/kid.0000000809

Safety and Effectiveness of Apixaban versus Warfarin by Kidney Function in Atrial Fibrillation

2025· article· en· W4409284021 on OpenAlexaffabout
Dickson Lam, Anish Scaria, Jason G. Andrade, Sunil V. Badve, Peter Birks, Sarah E. Bota, Anna Campain, Ognjenka Djurdjev, Amit X. Garg, Ziv Harel, Brenda R. Hemmelgarn, Carinna Hockham, Matthew T. James, Meg Jardine, Adeera Levin, Eric McArthur, Pietro Ravani, Selena Shao, Manish M. Sood, Zhi Tan, Navdeep Tangri, Reid Whitlock, Martin Gallagher, Min Jun, Jeffrey T. Ha

Bibliographic record

VenueKidney360 · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of ManitobaOttawa HospitalLibin Cardiovascular Institute of AlbertaUniversity of AlbertaLondon Health Sciences CentreSeven Oaks General HospitalSt. Michael's HospitalWestern UniversityVancouver General HospitalUniversity of CalgaryInstitute for Clinical Evaluative SciencesUniversity of British Columbia
FundersNational Health and Medical Research Council
KeywordsApixabanMedicineWarfarinAtrial fibrillationHazard ratioInternal medicineDialysisStroke (engine)Kidney diseaseRenal functionPropensity score matchingCardiologyRetrospective cohort studyProportional hazards modelConfidence intervalRivaroxaban

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.309
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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