MétaCan
Menu
← Back to cohort
Record W3107636597 · doi:10.1093/ehjci/ehaa946.3352

Impact of polyvascular disease and renal dysfunction on cardiovascular outcomes in diabetes: post hoc analyses from EMPA-REG OUTCOME

2020· article· en· W3107636597 on OpenAlexaff
Subodh Verma, C. David Mazer, Silvio E. Inzucchi, Christoph Wanner, Anne Pernille Ofstad, Odd Erik Johansen, Isabella Zwiener, Jyothis T. George, Javed Butler, Bernard Zinman

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMount Sinai HospitalUniversity of TorontoLunenfeld-Tanenbaum Research InstituteSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEmpagliflozinInternal medicinePost-hoc analysisRenal functionType 2 diabetesCardiologyPlaceboDiabetes mellitusCoronary artery diseaseStroke (engine)Proportional hazards modelEndocrinologyPathology

Abstract

fetched live from OpenAlex

Abstract Background Individuals with polyvascular disease and impaired renal function are at high risk of cardiovascular (CV) events, but this relationship is not well investigated in people with type 2 diabetes (T2D). Furthermore, the impact of polyvascular disease plus renal dysfunction on the risk for hospitalisation for heart failure (HHF) remains unclear. Purpose We investigated this in a post hoc analysis of the EMPA-REG OUTCOME trial in which empagliflozin reduced risk of CV death and HHF versus placebo in people with T2D and vascular disease. In addition, we explored the treatment effect of empagliflozin on CV, HF and mortality outcomes across the spectrum of baseline polyvascular disease and impaired renal function. Methods Patients with T2D, CV disease and estimated glomerular filtration rate (eGFR) of ≥30 ml/min/1.73 m2 received empagliflozin 10 mg, 25 mg, or placebo. Vascular beds (VBs) were defined as coronary artery disease, peripheral artery disease, and cerebrovascular disease (Fig). By use of Cox regression, we explored the association between baseline eGFR < or ≥60 ml/min/1.73 m2, with or without polyvascular disease (1 vs ≥2 VBs involved), and CV death, HHF, CV death (excl. fatal stroke)/HHF, and all-cause mortality (ACM), as well as the treatment effect of empagliflozin versus placebo on these outcomes. Results Patients with ≥2 VBs involved and eGFR <60 ml/min/1.73 m2 [n=463], were slightly older (mean age 68.2 vs. 64.3 or 62.6 years), had T2D duration >10 years more often (73.4% vs. 63.2% or 54.9%), and a higher HF prevalence at baseline (19.4% vs. 11.1% or 9.2%) versus those with ≥2 VBs involved and eGFR ≥60 ml/min/1.73 m2 [n=866], or those with only 1 VB involved regardless of eGFR [n=5630], respectively. However, characteristics were generally balanced between treatment groups. Notably, co-existing polyvascular disease and eGFR <60 ml/min/1.73 m2 was strongly associated with increased risk of all outcomes. The placebo incidence rates per 1000 patient-years for CV death were 14.4 (95% CI 10.9, 18.3) and 19.6 (12.8, 27.8) in those with 1 VB involved and eGFR ≥60 or eGFR <60, respectively, and 32.7 (21.7, 45.8), and 52.4 (32.9, 76.5) in those with 2 VBs and eGFR ≥60 or eGFR <60 ml/min/1.73 m2, respectively. Importantly, empagliflozin reduced the risk for all outcomes regardless of number of VBs affected and kidney function (Fig). Conclusions Co-existing polyvascular disease and eGFR <60 ml/min/1.73 m2 confer an extremely high risk of CV and all-cause mortality, and HHF. Empagliflozin lowered this risk consistently compared with placebo, regardless of polyvascular disease and impaired kidney function. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Boehringer Ingelheim and Eli Lilly and Company Diabetes Alliance

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)0.011
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.009
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.327
Teacher spread0.260 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Heart Journal→Same topicDiabetes Treatment and Management→French-language works237,207→