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Record W3164307722 · doi:10.1053/j.ajkd.2021.05.005

Canagliflozin and Kidney-Related Adverse Events in Type 2 Diabetes and CKD: Findings From the Randomized CREDENCE Trial

2021· article· en· W3164307722 on OpenAlexafffund
Hiddo J.L. Heerspink, Megumi Oshima, Hong Zhang, Jingwei Li, Rajiv Agarwal, George Capuano, David M. Charytan, Jagriti Craig, Dick de Zeeuw, Gian Luca Di Tanna, Adeera Levin, Bruce Neal, Vlado Perkovic, David C. Wheeler, Yshai Yavin, Meg Jardine

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of British Columbia
FundersJanssen Research and DevelopmentNational Health and Medical Research CouncilMedical Research CouncilNovo NordiskDaiichi Sankyo EuropeKidney Foundation of CanadaGilead SciencesServierNational Institute of Diabetes and Digestive and Kidney DiseasesAmgenPfizerZOLL Medical CorporationEli Lilly and CompanyAstraZenecaUniversity of WashingtonGlaxoSmithKline
KeywordsMedicineCanagliflozinCredenceType 2 diabetesRandomized controlled trialKidney diseaseAdverse effectDiabetes mellitusInternal medicineIntensive care medicineEndocrinology

Abstract

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Rationale & Objective Canagliflozin reduced the risk of kidney failure and related outcomes in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) in the CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) trial. This analysis of CREDENCE trial data examines the effect of canagliflozin on the incidence of kidney-related adverse events (AEs). Study Design A randomized, double-blind, placebo-controlled, multicenter international trial. Setting & Participants 4,401 trial participants with T2DM, CKD, and urinary albumin-creatinine ratio >300-5,000 mg/g. Interventions Participants were randomly assigned to receive canagliflozin 100 mg/d or placebo. Outcomes Rates of kidney-related AEs were analyzed using an on-treatment approach, overall and by screening estimated glomerular filtration rate (eGFR) strata (30-<45, 45-<60, and 60-<90 mL/min/1.73 m 2 ). Results Canagliflozin was associated with a reduction in the overall incidence rate of kidney-related AEs (60.2 vs 84.0 per 1,000 patient-years; hazard ratio [HR], 0.71 [95% CI, 0.61-0.82]; P < 0.001), with consistent results for serious kidney-related AEs (HR, 0.72 [95% CI, 0.51-1.00]; P = 0.05) and acute kidney injury (AKI; HR, 0.85 [95% CI, 0.64-1.13]; P = 0.3). The rates of kidney-related AEs were lower with canagliflozin relative to placebo across the 3 eGFR strata (HRs of 0.73, 0.60, and 0.81 for eGFR 30-<45, 45-<60, and 60-<90 mL/min/1.73 m 2 , respectively; P = 0.3 for interaction), with similar results for AKI ( P = 0.9 for interaction). Full recovery of kidney function within 30 days after an AKI event occurred more frequently with canagliflozin versus placebo (53.1% vs 35.4%; odds ratio, 2.2 [95% CI, 1.0-4.7]; P = 0.04). Limitations Kidney-related AEs including AKI were investigator-reported and collected without central adjudication. Biomarkers of AKI and structural tubular damage were not measured, and creatinine data after an AKI event were not available for all participants. Conclusions Compared with placebo, canagliflozin was associated with a reduced incidence of serious and nonserious kidney-related AEs in patients with T2DM and CKD. These results highlight the safety of canagliflozin with regard to adverse kidney-related AEs. Funding The CREDENCE trial and this analysis were funded by Janssen Research & Development, LLC, and were conducted as a collaboration between the funder, an academic steering committee, and an academic research organization, George Clinical. Trial Registration The CREDENCE trial was registered at ClinicalTrials.gov with identifier number NCT02065791.

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)0.019
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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 designRandomized trial
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

Citations52
Published2021
Admission routes2
Has abstractyes

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