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Record W2995255548 · doi:10.1093/cvr/cvz328

The DAPA-HF trial marks the beginning of a new era in the treatment of heart failure with reduced ejection fraction

2019· article· en· W2995255548 on OpenAlexaff
Subodh Verma

Bibliographic record

VenueCardiovascular Research · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsEjection fractionHeart failureCardiologyInternal medicineMedicine

Abstract

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Despite significant advances, heart failure remains a significant cause of death and disability worldwide. Unfortunately, the problem is growing and accordingly the cost of chronic care and hospitalization are escalating. Novel approaches are needed to reduce the burden of heart failure as well as improve the quality of life of these patients. The DAPA-HF (Study to Evaluate the Effect of Dapagliflozin on the Incidence of Worsening Heart Failure or Cardiovascular Death in Patients With Chronic Heart Failure) trial is a landmark study that marks the beginning of a completely new era in the treatment of heart failure with reduced ejection fraction.1 In this global study, 4744 patients with heart failure and reduced ejection fraction (EF < 45%) were randomized to receive the sodium-glucose transport protein 2 inhibitor (SGLT2i) dapagliflozin 10 mg daily or matching placebo. In addition to evidence of reduced ejection fraction, patients were enrolled if they were symptomatic despite optimal heart failure therapy, had evidence of moderately elevated natriuretic peptides levels, and had an estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73 m2. Diabetes was neither an inclusion nor exclusion criteria. Dapagliflozin reduced the primary outcome—a composite of worsening heart failure or cardiovascular death—by 26% [hazard ratio (HR) 0.74, 95% confidence interval (CI) 0.65–0.85; P < 0.001]. Individual components of the primary outcome were reduced—worsening heart failure by 30% (HR 0.70, 95% CI 0.59–0.83) and cardiovascular mortality by 18% (HR 0.82, 95% CI 0.69–0.98). Importantly, all-cause mortality was reduced by 17% (HR 0.83, 95% CI 0.71–0.97). The key findings are summarized in Figure 1. Remarkably, these benefits were observed in addition to excellent standard of care which included angiotensin-converting enzyme inhibitors (56%), angiotensin receptor blocker (28%), beta-blockers (96%), and mineralocorticoid receptor antagonists (72%). Furthermore, in post hoc analyses, consistent benefits were observed in those treated with and without sacubitril–valsartan (HR 0.75, 95% CI 0.50–1.13 and HR 0.74, 95% CI 0.65–0.86, respectively). Most intriguingly, the benefits were entirely consistent in those with and without type 2 diabetes (HR 0.75, 95% CI 0.63–0.90 among those with type 2 diabetes and HR 0.73, 95% CI 0.60–0.88 among those without type 2 diabetes) and were similar across the entire spectrum of glycated haemoglobin (A1C, assessed either continuously or categorically). From a safety standpoint, rates of discontinuation were low and similar between both groups, and there were no excess volume depletion or renal side effects. Treatment with dapagliflozin was also associated with a significant improvement in patient-reported outcomes as assessed by the Kansas City Cardiomyopathy Questionnaire.2 Recent sub-analyses from the trial demonstrate efficacy and safety across the broad range of patient age,3 eGFR,4 and ejection fraction.2 Strikingly, the benefit was statistically significant by 28 days after treatment initiation.5

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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.331
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2019
Admission routes1
Has abstractno

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