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Record W4377164053 · doi:10.1093/eurheartj/ehad344

Sacubitril/valsartan in heart failure with mildly reduced or preserved ejection fraction: a pre-specified participant-level pooled analysis of PARAGLIDE-HF and PARAGON-HF

2023· article· en· W4377164053 on OpenAlexaff
Muthiah Vaduganathan, Robert J. Mentz, Brian Claggett, Zi Michael Miao, Ian J. Kulac, Jonathan H. Ward, Adrian F. Hernandez, David A. Morrow, Randall C. Starling, Eric J. Velazquez, Kristin Williamson, Akshay S. Desai, Shelley Zieroth, Martin Lefkowitz, John J.V. McMurray, Eugene Braunwald, Scott D. Solomon

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Manitoba
FundersNational Center for Advancing Translational SciencesNovartis Pharmaceuticals Corporation
KeywordsSacubitrilSacubitril, ValsartanValsartanMedicineEjection fractionHeart failureInternal medicineClinical endpointCardiologyRenal functionContext (archaeology)Randomized controlled trialBlood pressure

Abstract

fetched live from OpenAlex

AIMS: The PARAGLIDE-HF trial demonstrated reductions in natriuretic peptides with sacubitril/valsartan compared with valsartan in patients with heart failure (HF) with mildly reduced or preserved ejection fraction who had a recent worsening HF event, but was not adequately powered to examine clinical outcomes. PARAGON-HF included a subset of PARAGLIDE-HF-like patients who were recently hospitalized for HF. Participant-level data from PARAGLIDE-HF and PARAGON-HF were pooled to better estimate the efficacy and safety of sacubitril/valsartan in reducing cardiovascular and renal events in HF with mildly reduced or preserved ejection fraction. METHODS AND RESULTS: Both PARAGLIDE-HF and PARAGON-HF were multicentre, double-blind, randomized, active-controlled trials of sacubitril/valsartan vs. valsartan in patients with HF with mildly reduced or preserved left ventricular ejection fraction (LVEF >40% in PARAGLIDE-HF and ≥45% in PARAGON-HF). In the pre-specified primary analysis, we pooled participants in PARAGLIDE-HF (all of whom were enrolled during or within 30 days of a worsening HF event) with a 'PARAGLIDE-like' subset of PARAGON-HF (those hospitalized for HF within 30 days). We also pooled the entire PARAGLIDE-HF and PARAGON-HF populations for a broader context. The primary endpoint for this analysis was the composite of total worsening HF events (including first and recurrent HF hospitalizations and urgent visits) and cardiovascular death. The secondary endpoint was the pre-specified renal composite endpoint for both studies (≥50% decline in estimated glomerular filtration rate from baseline, end-stage renal disease, or renal death). Compared with valsartan, sacubitril/valsartan significantly reduced total worsening HF events and cardiovascular death in both the primary pooled analysis of participants with recent worsening HF [n = 1088; rate ratio (RR) 0.78; 95% confidence interval (CI) 0.61-0.99; P = 0.042] and in the pooled analysis of all participants (n = 5262; RR 0.86; 95% CI: 0.75-0.98; P = 0.027). In the pooled analysis of all participants, first nominal statistical significance was reached by Day 9 after randomization, and treatment benefits were larger in those with LVEF ≤60% (RR 0.78; 95% CI 0.66-0.91) compared with those with LVEF >60% (RR 1.09; 95% CI 0.86-1.40; Pinteraction = 0.021). Sacubitril/valsartan was also associated with lower rates of the renal composite endpoint in the primary pooled analysis [hazard ratio (HR) 0.67; 95% CI 0.43-1.05; P = 0.080] and the pooled analysis of all participants (HR 0.60; 95% CI 0.44-0.83; P = 0.002). CONCLUSION: In pooled analyses of PARAGLIDE-HF and PARAGON-HF, sacubitril/valsartan reduced cardiovascular and renal events among patients with HF with mildly reduced or preserved ejection fraction. These data provide support for use of sacubitril/valsartan in patients with HF with mildly reduced or preserved ejection fraction, particularly among those with an LVEF below normal, regardless of care setting.

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)0.002
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 score0.145
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.122
GPT teacher head0.344
Teacher spread0.222 · 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

Citations120
Published2023
Admission routes1
Has abstractyes

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