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Effect of Dapagliflozin on Outpatient Worsening of Patients With Heart Failure and Reduced Ejection Fraction

2020· article· en· W3083153332 on OpenAlexafffund
Kieran F. Docherty, Pardeep S. Jhund, Inder S. Anand, Olof Bengtsson, Michael Böhm, Rudolf A. de Boer, David L. DeMets, Akshay S. Desai, Jarosław Dróżdż, Jonathan G. Howlett, Silvio E. Inzucchi, Per Johanson, Tzvetana Katova, Lars Køber, Mikhail Kosiborod, Anna Maria Langkilde, Daniel Lindholm, Felipe A. Martínez, Béla Merkely, José Carlos Nicolau, Eileen O’Meara, Piotr Ponikowski, Marc S. Sabatine, Mikaela Sjöstrand, Scott D. Solomon, С. Н. Терещенко, Subodh Verma, John J.V. McMurray

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalMontreal Heart InstituteLibin Cardiovascular Institute of Alberta
FundersAmerican RegentCumming School of Medicine, University of CalgaryInstitut de Cardiologie de MontréalServierUniwersytet Medyczny im. Piastów Slaskich we WroclawiuVifor PharmaUniversität des SaarlandesUniversitair Medisch Centrum GroningenNovo NordiskDaiichi-SankyoGentofte HospitalSemmelweis EgyetemUniversity of MinnesotaSaint Luke's Health SystemUniversidade de São PauloAstraZenecaAmarin CorporationUniwersytet ŁódzkiIntarcia TherapeuticsRegeneron PharmaceuticalsDeutsche ForschungsgemeinschaftZafgenUniversidad Nacional de CórdobaBoston Scientific CorporationAlnylam PharmaceuticalsAlereUniversity of TorontoUniversity of New South WalesUniversity of GlasgowYale UniversityPfizerUniversity of MissouriBritish Heart FoundationEisaiCytokineticsBrigham and Women's HospitalNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiUniversity of Missouri-Kansas CityGlaxoSmithKlineAmgenEli Lilly and CompanyRijksuniversiteit GroningenBristol-Myers Squibb
KeywordsMedicineEjection fractionDapagliflozinHeart failureCardiologyInternal medicineDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: In the DAPA-HF trial (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure), dapagliflozin, added to guideline-recommended therapies, reduced the risk of mortality and heart failure (HF) hospitalization. We examined the frequency and significance of episodes of outpatient HF worsening, requiring the augmentation of oral therapy, and the effects of dapagliflozin on these additional events. METHODS: Patients in New York Heart Association functional class II to IV, with a left ventricular ejection fraction ≤40% and elevation of NT-proBNP (N-terminal pro-B-type natriuretic peptide), were eligible. The primary outcome was the composite of an episode of worsening HF (HF hospitalization or an urgent HF visit requiring intravenous therapy) or cardiovascular death, whichever occurred first. An additional prespecified exploratory outcome was the primary outcome plus worsening HF symptoms/signs leading to the initiation of new, or the augmentation of existing, oral treatment. RESULTS: <0.0001). Each component of the composite was reduced significantly by dapagliflozin. Over the median follow-up of 18.2 months, the number of patients needed to treat with dapagliflozin to prevent 1 experiencing an episode of fatal or nonfatal worsening was 16. Among the 4744 randomly assigned patients, the first episode of worsening was outpatient augmentation of treatment in 407 participants (8.6%), an urgent HF visit with intravenous therapy in 20 (0.4%), HF hospitalization in 489 (10.3%), and cardiovascular death in 295 (6.2%). The adjusted risk of death from any cause (in comparison with no event) after an outpatient worsening was hazard ratio, 2.67 (95% CI, 2.03-3.52); after an urgent HF visit, the adjusted risk of death was hazard ratio, 3.00 (95% CI, 1.39-6.48); and after a HF hospitalization, the adjusted risk of death was hazard ratio, 6.21 (95% CI, 5.07-7.62). CONCLUSION: In DAPA-HF, outpatient episodes of HF worsening were common, were of prognostic importance, and were reduced by dapagliflozin. 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 score0.013
Threshold uncertainty score0.223

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations89
Published2020
Admission routes2
Has abstractyes

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