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Record W4229036077 · doi:10.1093/ndt/gfac126.001

FC 122: Effects of Canagliflozin on Cardiovascular and Kidney Events in Patients With Chronic Kidney Disease With and Without Peripheral Vascular Disease: Integrated Analysis From the Canvas Program and Credence Trial

2022· article· en· W4229036077 on OpenAlexaff
Adeera Levin, Paul Poirier, Jochen Seufert, April Slee, Wally Rapattoni, Fernando Ang, David C. Wheeler

Bibliographic record

VenueNephrology Dialysis Transplantation · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of British Columbia
Fundersnot available
KeywordsCanagliflozinMedicineKidney diseaseHazard ratioRenal functionInternal medicinePlaceboEmpagliflozinDiabetes mellitusType 2 diabetesEndocrinologyConfidence intervalPathology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND AND AIMS Type 2 diabetes mellitus (T2DM) is associated with comorbidities, such as chronic kidney disease (CKD) and peripheral vascular disease (PVD), which may increase risk of cardiovascular (CV) and kidney events. Canagliflozin, a sodium glucose co-transporter 2 (SGLT2) inhibitor, reduced the risk of CV and kidney events in patients with T2DM and high CV risk or nephropathy in the CANVAS Program and CREDENCE trial, respectively. The effects of canagliflozin on CV and kidney outcomes in patients with CKD with and without PVD remain unknown. METHOD This post hoc analysis included integrated, pooled data from the CANVAS Program and the CREDENCE trial. The effects of canagliflozin compared with placebo on CV and kidney outcomes were assessed in patients with CKD with and without PVD at baseline. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and PVD was defined based on investigator classification on the electronic case report form without requirement for specific clinical evaluation or imaging. Propensity score (PS) matching was used to balance patient demographics and baseline clinical characteristics between groups. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated using Cox regression models. RESULTS A total of 14 543 participants from the CANVAS Program (N = 10 142) and CREDENCE (N = 4401) were included. Of these, 3514 had CKD alone (canagliflozin, n = 1792; placebo, n = 1722; mean eGFR, 46 mL/min/1.73 m2; symptomatic CV disease history, 49%; insulin use, 64%) and 1156 had CKD + PVD (canagliflozin, n = 626; placebo, n = 530; mean eGFR, 46 mL/min/1.73 m2; symptomatic CV disease history, 96%; insulin use, 74%) at baseline. Canagliflozin was associated with a reduced risk of major adverse cardiovascular events (MACE), the composite of hospitalization for heart failure (HHF) or CV death (HHF/CV death), doubling of serum creatinine (dSCr), end-stage kidney disease (ESKD) and the composite of ESKD or dSCr compared with placebo in patients with CKD with and without PVD (Figure 1A). After matching, 3210 patients had CKD alone (mean eGFR, 46 mL/min/1.73 m2; symptomatic CV disease history, 49%; insulin use, 64%) and 966 had CKD + PVD (mean eGFR, 46 mL/min/1.73 m2; symptomatic CV disease history, 98%; insulin use, 74%), with equal numbers in the canagliflozin and placebo groups. In the PS-matched groups, canagliflozin was associated with a reduced risk of MACE, HHF/CV death, dSCr, ESKD and the composite of ESKD or dSCr compared with placebo in patients with CKD with and without PVD (Figure 1B). CONCLUSION Canagliflozin significantly reduced the risk of MACE, HHF/CV death, dSCr, ESKD, and the composite of ESKD or dSCr in patients with CKD with and without PVD, suggesting that the beneficial effects of canagliflozin on CV and kidney outcomes are consistent and can be seen in patients regardless of these comorbidities.

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)0.006
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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
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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Citations0
Published2022
Admission routes1
Has abstractyes

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