MétaCan
Menu
Back to cohort

Efficacy of Dapagliflozin on Renal Function and Outcomes in Patients With Heart Failure With Reduced Ejection Fraction

2020· article· en· W3092582090 on OpenAlexafffund
Pardeep S. Jhund, Scott D. Solomon, Kieran F. Docherty, Hiddo J.L. Heerspink, Inder S. Anand, Michael Böhm, Vijay Chopra, Rudolf A. de Boer, Akshay S. Desai, Junbo Ge, Masafumi Kitakaze, Bela Merkley, Eileen O’Meara, Morten Shou, С. Н. Терещенко, Subodh Verma, Pham Nguyễn Vinh, Silvio E. Inzucchi, Lars Køber, Mikhail Kosiborod, Felipe A. Martínez, Piotr Ponikowski, Marc S. Sabatine, Olof Bengtsson, Anna Maria Langkilde, Mikaela Sjöstrand, John J.V. McMurray

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalMontreal Heart Institute
FundersRelypsaAmerican RegentJapan Heart FoundationServierIntarcia TherapeuticsUniwersytet Medyczny im. Piastów Slaskich we WroclawiuGlaxoSmithKlineUniversität des SaarlandesInstitut de Cardiologie de MontréalShionogiRigshospitaletNovo NordiskFudan UniversityGentofte HospitalBristol-Myers SquibbSemmelweis EgyetemBritish Heart FoundationZora BiosciencesSaint Luke's Health SystemAlnylam PharmaceuticalsZafgenAstraZenecaAmarin CorporationRegeneron PharmaceuticalsUniversidad Nacional de CórdobaDefense Acquisition Program AdministrationBoston Scientific CorporationAlereUniversity of MinnesotaUniversity of TorontoUniversity of New South WalesToa EiyoUniversity of GlasgowDaiichi Sankyo EuropeYale UniversityPfizerUniversity of MissouriEisaiCytokineticsBrigham and Women's HospitalNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiNational Cerebral and Cardiovascular CenterUniversitair Medisch Centrum GroningenKowa CompanyAmgenEli Lilly and CompanyRijksuniversiteit Groningen
KeywordsMedicineEjection fractionDapagliflozinHeart failureCardiologyRenal functionInternal medicineDiabetes mellitusEndocrinologyType 2 diabetes

Abstract

fetched live from OpenAlex

Background: Many patients with heart failure and reduced ejection fraction (HFrEF) have chronic kidney disease that complicates pharmacological management and is associated with worse outcomes. We assessed the safety and efficacy of dapagliflozin in patients with HFrEF, according to baseline kidney function, in the DAPA-HF trial (Dapagliflozin and Prevention of Adverse-outcomes in Heart Failure). We also examined the effect of dapagliflozin on kidney function after randomization. Methods: Patients who have HFrEF with or without type 2 diabetes and an estimated glomerular filtration rate (eGFR) ≥30 mL·min –1 ·1.73 m –2 were enrolled in DAPA-HF. We calculated the incidence of the primary outcome (cardiovascular death or worsening heart failure) according to eGFR category at baseline (<60 and ≥60 mL·min –1 ·1.73 m –2 ) and used eGFR at baseline as a continuous measure, as well. Secondary cardiovascular outcomes and a prespecified composite renal outcome (≥50% sustained decline eGFR, end-stage renal disease, or renal death) were also examined, along with a decline in eGFR over time. Results: Of 4742 patients with a baseline eGFR, 1926 (41%) had eGFR <60 mL·min –1 ·1.73 m –2 . The effect of dapagliflozin on the primary and secondary outcomes did not differ by eGFR category or examining eGFR as a continuous measurement. The hazard ratio (95% CI) for the primary end point in patients with chronic kidney disease was 0.71 (0.59–0.86) versus 0.77 (0.64–0.93) in those with an eGFR ≥60 mL·min –1 ·1.73 m –2 (interaction P =0.54). The composite renal outcome was not reduced by dapagliflozin (hazard ratio=0.71 [95% CI, 0.44–1.16]; P =0.17) but the rate of decline in eGFR between day 14 and 720 was less with dapagliflozin, –1.09 (–1.40 to –0.77) versus placebo –2.85 (–3.17 to –2.53) mL·min –1 ·1.73 m –2 per year ( P <0.001). This was observed in those with and without type 2 diabetes ( P for interaction=0.92). Conclusions: Baseline kidney function did not modify the benefits of dapagliflozin on morbidity and mortality in HFrEF, and dapagliflozin slowed the rate of decline in eGFR, including in patients without diabetes. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03036124.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 teacher head, 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

Citations303
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCirculationSame topicDiabetes Treatment and ManagementFrench-language works237,207