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Efficacy and Safety of Dapagliflozin in Type 2 Diabetes According to Baseline Blood Pressure: Observations From DECLARE-TIMI 58 Trial

2022· article· en· W4229055197 on OpenAlexaff
Remo H.M. Furtado, Itamar Raz, Erica L. Goodrich, Sabina A. Murphy, Deepak L. Bhatt, Lawrence A. Leiter, Darren K. McGuire, John Wilding, Philip E. Aylward, Anthony J. Dalby, Mikael Dellborg, Doina Dimulescu, José Carlos Nicolau, A. J. M. Oude Ophuis, Avivit Cahn, Ofri Mosenzon, Ingrid Gause‐Nilsson, Anna Maria Langkilde, Marc S. Sabatine, Stephen D. Wiviott

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDapagliflozinTIMIInternal medicineKidney diseaseBlood pressureHazard ratioMyocardial infarctionCardiologyHeart failureType 2 diabetesDiabetes mellitusRenal functionPlaceboThrombolysisEndocrinologyConfidence intervalPathology

Abstract

fetched live from OpenAlex

Background: Dapagliflozin improved heart failure and kidney outcomes in patients with type 2 diabetes (T2DM) with or at high risk for atherosclerotic cardiovascular disease in the DECLARE-TIMI 58 trial (Dapagliflozin Effect on Cardiovascular Events – Thrombolysis in Myocardial Infarction 58). Here, the aim was to analyze the efficacy and safety of dapagliflozin stratified according to baseline systolic blood pressure (SBP). Methods: The DECLARE-TIMI 58 trial randomly assigned patients with T2DM and either previous atherosclerotic cardiovascular disease or atherosclerotic cardiovascular disease risk factors to dapagliflozin or placebo. Patients were categorized by baseline SBP levels: <120, 120 to 129, 130 to 139, 140 to 159, and ≥160 mm Hg (normal, elevated, stage 1, stage 2, and severe hypertension, respectively). Efficacy outcomes of interest were hospitalization for heart failure and a renal-specific composite outcome (sustained decrease in estimated glomerular filtration rate by 40%, progression to end-stage renal disease, or renal death). Safety outcomes included symptoms of volume depletion, lower extremity amputations, and acute kidney injury. Results: The trial comprised 17 160 patients; mean age, 64.0±6.8 years; 37.4% women; median duration of T2DM, 11 years; 40.6% with prevalent cardiovascular disease. Overall, dapagliflozin reduced SBP by 2.4 mm Hg (95% CI, 1.9–2.9; P <0.0001) compared with placebo at 48 months. The beneficial effects of dapagliflozin on hospitalization for heart failure and renal outcomes were consistent across all baseline SBP categories, with no evidence of modification of treatment effect ( P interactions =0.28 and 0.52, respectively). Among normotensive patients, the hazard ratios were 0.66 (95% CI, 0.42–1.05) and 0.39 (95% CI, 0.19–0.78), respectively, for hospitalization for heart failure and the renal-specific outcome. Events of volume depletion, amputation, and acute kidney injury did not differ with dapagliflozin overall or within any baseline SBP group. Conclusions: In patients with T2DM with or at high atherosclerotic cardiovascular disease risk, dapagliflozin reduced risk for hospitalization for heart failure and renal outcomes regardless of baseline SBP, with no difference in adverse events of interest at any level of baseline SBP. These results indicate that dapagliflozin provides cardiorenal benefits in patients with T2DM at high atherosclerotic cardiovascular disease risk independent of baseline blood pressure. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01730534.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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".

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Citations26
Published2022
Admission routes1
Has abstractyes

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