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Oral anticoagulation in patients with atrial fibrillation and stage IV and V chronic kidney disease: a systematic review and meta-analysis

2022· review· en· W4306319913 on OpenAlexaff
Andrés F. Miranda‐Arboleda, A I Davila-Calle, Diego Fernando Rojas‐Gualdrón, Juan G. Sierra-David, F Villegas

Bibliographic record

VenueEuropean Heart Journal · 2022
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineApixabanInternal medicineRandomized controlled trialAtrial fibrillationStroke (engine)WarfarinKidney diseaseObservational studyDabigatranMeta-analysisRivaroxabanRelative riskClinical endpointIntensive care medicineConfidence interval

Abstract

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Abstract Introduction The association between atrial fibrillation (AF) and chronic kidney disease (CKD) is directly proportional and carries a higher risk of death, bleeding, and thromboembolism (TE). There is uncertainty on the clinical benefit of direct oral anticoagulants (DOAC) in patients with advanced stages of CKD. Purpose To perform a systematic review and meta-analysis of the available literature on the efficacy and safety of DOAC compared to warfarin in patients with AF and stage IV and V CKD with or without hemodialysis. Methods We reviewed PubMed, SCOPUS, EMBASE, LILACS, Google Scholar, Open Gray, and Clinical Trials until April 30th, 2021. A standardized search was performed independently by 2 authors. The primary efficacy endpoint was the reduction of TE (stroke or systemic embolism). The primary safety outcome was major bleeding, additional bleeding outcomes were analyzed as well. We included randomized controlled trials and observational intervention studies. Results We analyzed data from 80,771 patients participating in 14 studies (2 sub-analyses of randomized controlled trials and 12 observational studies). The risk of bias was low, moderate, and high in 2, 6, and 6 studies, respectively. We did not find any difference between the use of DOAC versus warfarin to reduce the risk of TE; there was a trend but non statistically significant though (RR: 0.88 [95% CI, 0.72–1.06], I2=18.7%). Apixaban showed similar behaviour in the reduction of TE with low heterogeneity (RR: 0.86 [95% CI, 0.68–1.08], I2=0%). Conversely, dabigatran (RR: 0.73 [95% CI, 0.37–1.42], I2=17%) and rivaroxaban (RR: 1.08 [95% CI, 0.62–1.89], I2=49%) showed neutral distribution and had higher heterogeneity (Figure 1). The risk of major bleeding was not reduced significantly by DOAC compared to warfarin (RR: 0.9 [95% CI, 0.67–1.21], I2=80.6%), apixaban showed a trend to have a lower risk of major bleeding, but it was not statistically significant (RR: 0.7 [95% CI, 0.46–1.08], I2=53.3%). Rivaroxaban expressed neutral outcome (RR: 0.94 [95% CI, 0.66–1.35], I2=68.3%), and dabigatran had a clear inclination to increase major bleeding (RR: 1.42 [95% CI, 0.99–2.04], I2=6.6%) (Figure 2). Dabigatran significantly increased the risk of gastrointestinal bleeding in our studied population (RR: 1.49 [95% CI, 1.08–2.05]), although this conclusion was obtained from one study. Conclusion The use of DOAC compared to warfarin in patients with AF and stage IV and V CKD did not significantly reduce the rate of TE. There was a trend to reduce this outcome with apixaban, but it did not reach significance due to the inclusion in our study of patients in hemodialysis that represent a higher risk subgroup of patients. Major bleeding showed a similar pattern, expressing a non-statistically significant trend to be reduced in the DOAC group, especially with apixaban. Dabigatran increased the risk of bleeding by 49% in the studied population. Funding Acknowledgement Type of funding sources: None.

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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)0.012
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0200.029
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.153
GPT teacher head0.379
Teacher spread0.226 · 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 designMeta-analysis
Domainnot available
GenreReview

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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Citations0
Published2022
Admission routes1
Has abstractyes

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