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Record W4214940159 · doi:10.1038/s41591-021-01659-1

The SGLT2 inhibitor empagliflozin in patients hospitalized for acute heart failure: a multinational randomized trial

2022· article· en· W4214940159 on OpenAlexafffund
Adriaan A. Voors, Christiane E. Angermann, John R. Teerlink, Sean P. Collins, Mikhail Kosiborod, Jan Biegus, João Pedro Ferreira, Michael E. Nassif, Mitchell A. Psotka, Jasper Tromp, C. Jan Willem Borleffs, Changsheng Ma, J COMINCOLET, Michael Fu, Stefan Janssens, Róbert Gábor Kiss, Robert J. Mentz, Yasushi Sakata, Henrik Schirmer, Morten Schou, P. Christian Schulze, Lenka Špinarová, Maurizio Volterrani, Jerzy Krzysztof Wranicz, Uwe Zeymer, Shelley Zieroth, Martina Brueckmann, Jon Blatchford, Afshin Salsali, Piotr Ponikowski

Bibliographic record

VenueNature Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Manitoba
FundersDuke Clinical Research InstituteMax Rady College of Medicine, University of ManitobaAkershus UniversitetssykehusSahlgrenska UniversitetssjukhusetCapital Medical UniversityRijksuniversiteit GroningenInstitut National de la Santé et de la Recherche MédicaleGöteborgs UniversitetBoehringer IngelheimNational University of SingaporeSchool of Medicine, University of MissouriUniversität HeidelbergMasarykova UniverzitaUniversidade do PortoUniwersytet ŁódzkiVanderbilt University Medical CenterKU LeuvenUniversity of MissouriUniversity of Missouri-Kansas CitySaint Luke's Health SystemUniversité de LorraineVanderbilt UniversityUniversitair Medisch Centrum GroningenEli Lilly and CompanyNational University Health SystemUniversity of New South WalesU.S. Department of Veterans Affairs
KeywordsEmpagliflozinMedicineHeart failureEjection fractionClinical endpointPlaceboInternal medicineRandomized controlled trialAcute decompensated heart failureCardiologyHazard ratioConfidence intervalRandomizationClinical trialDiabetes mellitusType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

The sodium-glucose cotransporter 2 inhibitor empagliflozin reduces the risk of cardiovascular death or heart failure hospitalization in patients with chronic heart failure, but whether empagliflozin also improves clinical outcomes when initiated in patients who are hospitalized for acute heart failure is unknown. In this double-blind trial (EMPULSE; NCT04157751 ), 530 patients with a primary diagnosis of acute de novo or decompensated chronic heart failure regardless of left ventricular ejection fraction were randomly assigned to receive empagliflozin 10 mg once daily or placebo. Patients were randomized in-hospital when clinically stable (median time from hospital admission to randomization, 3 days) and were treated for up to 90 days. The primary outcome of the trial was clinical benefit, defined as a hierarchical composite of death from any cause, number of heart failure events and time to first heart failure event, or a 5 point or greater difference in change from baseline in the Kansas City Cardiomyopathy Questionnaire Total Symptom Score at 90 days, as assessed using a win ratio. More patients treated with empagliflozin had clinical benefit compared with placebo (stratified win ratio, 1.36; 95% confidence interval, 1.09-1.68; P = 0.0054), meeting the primary endpoint. Clinical benefit was observed for both acute de novo and decompensated chronic heart failure and was observed regardless of ejection fraction or the presence or absence of diabetes. Empagliflozin was well tolerated; serious adverse events were reported in 32.3% and 43.6% of the empagliflozin- and placebo-treated patients, respectively. These findings indicate that initiation of empagliflozin in patients hospitalized for acute heart failure is well tolerated and results in significant clinical benefit in the 90 days after starting treatment.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.279
Teacher spread0.275 · 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

Citations895
Published2022
Admission routes2
Has abstractyes

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