MétaCan
Menu
← Back to cohort
Record W4213345015 · doi:10.21203/rs.3.rs-144190/v1

Stroke and Major Bleeding when Switching from Warfarin to Apixaban in Patients with Advanced Chronic Kidney Disease and Prevalent Atrial Fibrillation

2021· preprint· en· W4213345015 on OpenAlexaff
Katherine Garlo, Thomas A. Mavrakanas, Wei Wang, Elisabeth Burdick, David M. Charytan

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsApixabanMedicineWarfarinAtrial fibrillationKidney diseaseInternal medicineStroke (engine)CardiologyDialysisIncidence (geometry)Rivaroxaban

Abstract

fetched live from OpenAlex

Abstract Background Apixaban is the most widely used direct oral anticoagulant in patients with chronic kidney disease (CKD). Data on the incidence of stroke and major bleeding after switching from warfarin to apixaban in patients with prevalent atrial fibrillation (AF) and CKD are limited.Methods Warfarin users with stage 4-5 CKD not on dialysis and non-valvular AF prior to Jan 1,2012 were identified from the United States Data Renal System CKD dataset and individuals switching to apixaban from Jan 1,2012 -Dec 31, 2015 were identified. The incidence of stroke (ischemic or hemorrhagic), transient ischemic attack, or systemic thromboembolism and major bleeding events were estimated. Outcomes were compared between individuals switching to apixaban and those continuing warfarin using survival analyses with inverse probability treatment weighting. Individuals were censored at the time of anticoagulation discontinuation, loss of Medicare part D coverage, dialysis, kidney transplant, a 2nd switch in anticoagulant class, or death. Results 1762 individuals with advanced CKD and AF were initially on warfarin; 71 (4.0%) switched to apixaban (57.8% male, mean age 78.2 years (SD ±6.6), 78.9% white, mean CHA2DS2-VASc 5.0 (SD ±1.5), mean HAS-BLED 2.2 (SD ±0.5) and 1691 (96.0%) continued warfarin (47.6% male, mean age 80.1 years (SD ±8.7), 87.9% white, mean CHA2DS2-VASc 5.5 (SD ±1.6), mean HAS-BLED 2.5 (SD ±0.8). The incidence of stroke in the apixaban switch and warfarin continuation groups were 0.02/patient-year (95%CI 0.002-0.12) and 0.06/patient-year (95%CI 0.05-0.07) (p=0.21). Incidence of major bleeding were 0.02/patient-year (95% CI 0.002-0.13) and 0.06 (95% CI 0.03-0.04) (p =0.44) in the switch and warfarin groups, respectively. In adjusted models, the risk of stroke (HR 0.27 (95% CI 0.04-1.99)) and major bleeding (HR 0.41 (95% CI 0.06-3.02)) trended lower in the apixaban switch compared to the warfarin continuation group.Conclusions The incidence and risk of stroke and major bleeding trended lower in individuals with stage 4-5 CKD and prevalent AF who switched from warfarin to apixaban than individuals continuing warfarin. Our findings support a strategy of switching prevalent AF patients with late stage CKD from warfarin to apixaban. Additional studies including a larger number of events with a longer-duration of follow-up are needed to refine effect estimates.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.366
Teacher spread0.317 · 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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueResearch Square→Same topicAtrial Fibrillation Management and Outcomes→French-language works237,207→