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Comparative Risk of Acute Kidney Injury with Oral Anticoagulant Use Among Patients with Nonvalvular Atrial Fibrillation

2017· article· en· W2971754743 on OpenAlexaffabout
Adi J. Klil‐Drori, Laurent Azoulay, Rui Nie, Christel Renoux, Sharon J. Nessim, Kristian B. Filion

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsApixabanMedicineRivaroxabanDabigatranAtrial fibrillationInternal medicineWarfarinHazard ratioKidney diseaseAcute kidney injuryCohortProportional hazards modelConfidence interval

Abstract

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Abstract Background: Acute kidney injury (AKI) is common in patients with nonvalvular atrial fibrillation (NVAF) taking oral anticoagulants (OAC), especially in those with underlying chronic kidney disease (CKD). However, little is known on the comparative risk of AKI with the use of different OAC, including the direct acting oral anticoagulants (DOACs). Methods: We conducted a population-based cohort study using the administrative healthcare databases of Quebec, Canada. Patients with NVAF who were new users of oral anticoagulants between 2011 and 2014 were followed for hospitalized AKI, stratified by CKD status. Hazard ratios (HR) and corresponding 95% CI for hospitalized AKI with the use of dabigatran, rivaroxaban, and apixaban versus warfarin and versus each other in pairwise analyses, were assessed using Cox proportional hazards models. Estimates were stratified by the deciles of a disease risk score for AKI obtained from a historic cohort of 25,513 warfarin users with NVAF. The disease risk score included important demographic and clinical covariates weighted by their association with AKI. Results: Our cohort of 26,357 patients with NVAF included 22,084 without, and 4273 with CKD. The rates of AKI hospitalization were 3.2 (95% CI 2.9-3.4) and 16.5 (95% CI 15.3-17.9) per 100 person-years, respectively. Among patients without CKD, apixaban (HR, 0.25; 95% CI, 0.14-0.44), rivaroxaban (HR, 0.50; 95% CI, 0.40-0.64), and dabigatran (HR, 0.68; 95% CI 0.57-0.81) were associated with a decreased risk of AKI compared with warfarin (Table 1). Similar findings were observed in the CKD cohort (Table 2). Compared with apixaban, dabigatran was associated with increased AKI risk in patients without (HR, 2.58; 95% CI, 1.42-4.67) and with (HR, 2.08; 95% CI, 1.01-4.26) CKD, as was rivaroxaban (without CKD - HR, 2.29 [95% CI, 1.24-4.20]; with CKD - HR, 1.98 [95% CI, 0.95-4.11]). Rivaroxaban was associated with decreased risk of AKI in patients without CKD (HR, 0.76; 95% CI, 0.58-0.99), compared with dabigatran. Conclusions: Use of all DOACs was associated with decreased risk of AKI compared with warfarin. Apixaban was associated with the lowest comparative risk of AKI among DOACs, and rivaroxaban was suggested to be more protective than dabigatran in patients without CKD. While these findings await further confirmation, worsening renal function while on DOAC treatment has been associated with increased risk of major bleeding. Thus, suggested differences in the renal safety of DOACs should be incorporated in informed prescription and monitoring of these drugs. Disclosures Renoux: Bayer Pharmaceuticals: Research Funding; Canadian Foundation for Innovation: Research Funding.

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)0.003
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 score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.039
GPT teacher head0.313
Teacher spread0.273 · 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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Citations3
Published2017
Admission routes2
Has abstractyes

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