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Outcomes Associated With Apixaban Use in Patients With End-Stage Kidney Disease and Atrial Fibrillation in the United States

2018· article· en· W2897495391 on OpenAlexaff
Konstantinos C. Siontis, Xiaosong Zhang, Ashley Eckard, Nicole M. Bhave, Douglas E. Schaubel, Kevin He, Anca Tilea, Austin G. Stack, Rajesh Balkrishnan, Xiaoxi Yao, Peter A. Noseworthy, Nilay D. Shah, Rajiv Saran, Brahmajee K. Nallamothu

Bibliographic record

VenueCirculation · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsThe Quebec Population Health Research Network
FundersU.S. National Library of Medicine
KeywordsApixabanMedicineRivaroxabanDabigatranWarfarinAtrial fibrillationInternal medicineStroke (engine)DialysisHazard ratioKidney diseaseEnd stage renal diseasePopulationProportional hazards modelRetrospective cohort studyIntensive care medicineDiseaseConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with end-stage kidney disease (ESKD) on dialysis were excluded from clinical trials of direct oral anticoagulants for atrial fibrillation (AF). Recent data have raised concerns regarding the safety of dabigatran and rivaroxaban, but apixaban has not been evaluated despite current labeling supporting its use in this population. The goal of this study was to determine patterns of apixaban use and its associated outcomes in dialysis-dependent patients with ESKD and AF. METHODS: We performed a retrospective cohort study of Medicare beneficiaries included in the United States Renal Data System (October 2010 to December 2015). Eligible patients were those with ESKD and AF undergoing dialysis who initiated treatment with an oral anticoagulant. Because of the small number of dabigatran and rivaroxaban users, outcomes were only assessed in patients treated with apixaban or warfarin. Apixaban and warfarin patients were matched (1:3) based on prognostic score. Differences between groups in survival free of stroke or systemic embolism, major bleeding, gastrointestinal bleeding, intracranial bleeding, and death were assessed using Kaplan-Meier analyses. Hazard ratios (HRs) and 95% CIs were derived from Cox regression analyses. RESULTS: The study population consisted of 25 523 patients (45.7% women; 68.2±11.9 years of age), including 2351 patients on apixaban and 23 172 patients on warfarin. An annual increase in apixaban prescriptions was observed after its marketing approval at the end of 2012, such that 26.6% of new anticoagulant prescriptions in 2015 were for apixaban. In matched cohorts, there was no difference in the risks of stroke/systemic embolism between apixaban and warfarin (HR, 0.88; 95% CI, 0.69-1.12; P=0.29), but apixaban was associated with a significantly lower risk of major bleeding (HR, 0.72; 95% CI, 0.59-0.87; P<0.001). In sensitivity analyses, standard-dose apixaban (5 mg twice a day; n=1034) was associated with significantly lower risks of stroke/systemic embolism and death as compared with either reduced-dose apixaban (2.5 mg twice a day; n=1317; HR, 0.61; 95% CI, 0.37-0.98; P=0.04 for stroke/systemic embolism; HR, 0.64; 95% CI, 0.45-0.92; P=0.01 for death) or warfarin (HR, 0.64; 95% CI, 0.42-0.97; P=0.04 for stroke/systemic embolism; HR, 0.63; 95% CI, 0.46-0.85; P=0.003 for death). CONCLUSIONS: Among patients with ESKD and AF on dialysis, apixaban use may be associated with a lower risk of major bleeding compared with warfarin, with a standard 5 mg twice a day dose also associated with reductions in thromboembolic and mortality risk.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.287
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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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Citations487
Published2018
Admission routes1
Has abstractyes

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