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Record W3037737029 · doi:10.1002/ejhf.1869

Effects of Combined Renin–Angiotensin–Aldosterone System Inhibitor and Beta-Blocker Treatment on Outcomes in Heart Failure with Reduced Ejection Fraction: Insights from BIOSTAT-CHF and ASIAN-HF Registries

2020· article· en· W3037737029 on OpenAlexaff
Wouter Ouwerkerk, Tiew‐Hwa Katherine Teng, Jasper Tromp, Wan Ting Tay, John G.F. Cleland, Dirk J. van Veldhuisen, Kenneth Dickstein, Leong L. Ng, Chim C. Lang, Stefan D. Anker, Faı̈ez Zannad, Chung‐Lieh Hung, J.P.S. Sawhney, Ajay Naik, Wataru Shimizu, Nobuhisa Hagiwara, Gurpreet Singh Wander, Inder S. Anand, Mark Richards, Adriaan A. Voors, Carolyn S.P. Lam

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsInstitute of Infection and Immunity
FundersNational Medical Research CouncilBoston Scientific CorporationBiomedical Research CouncilVifor PharmaServierEuropean CommissionMedical Research CouncilAlereAbbott Laboratories
KeywordsMedicineEjection fractionHeart failureHazard ratioInternal medicineCardiologyConfidence intervalBeta blockerGuidelineAngiotensin receptorRenin–angiotensin systemBlood pressure

Abstract

fetched live from OpenAlex

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARB) and β-blockers are guideline-recommended first-line therapies in heart failure (HF) with reduced ejection fraction (HFrEF). Previous studies showed that individual drug classes were under-dosed in many parts of Europe and Asia. In this study, we investigated the association of combined up-titration of ACEi/ARBs and β-blockers with all-cause mortality and its combination with hospitalization for HF. METHODS AND RESULTS: A total of 6787 HFrEF patients (mean age 62.6 ± 13.2 years, 77.7% men, mean left ventricular ejection fraction 27.7 ± 7.2%) were enrolled in the prospective multinational European (BIOSTAT-CHF; n = 2100) and Asian (ASIAN-HF; n = 4687) studies. Outcomes were analysed according to achieved percentage of guideline-recommended target doses (GRTD) of combination ACEi/ARB and β-blocker therapy, adjusted for indication bias. Only 14% (n = 981) patients achieved ≥50% GRTD for both ACEi/ARB and β-blocker. The best outcomes were observed in patients who achieved 100% GRTD of both ACEi/ARB and β-blocker [hazard ratio (HR) 0.32, 95% confidence interval (CI) 0.26-0.39 vs. none]. Lower dose of combined therapy was associated with better outcomes than 100% GRTD of either monotherapy. Up-titrating β-blockers was associated with a consistent and greater reduction in hazards of all-cause mortality (HR for 100% GRTD: 0.40, 95% CI 0.25-0.63) than corresponding ACEi/ARB up-titration (HR 0.75, 95% CI 0.53-1.07). CONCLUSION: This study shows that best outcomes were observed in patients attaining GRTD for both ACEi/ARB and β-blockers, unfortunately this was rarely achieved. Achieving >50% GRTD of both drug classes was associated with better outcome than target dose of monotherapy. Up-titrating β-blockers to target dose was associated with greater mortality reduction than up-titrating ACEi/ARB.

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)0.009
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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".

Quick stats

Citations36
Published2020
Admission routes1
Has abstractyes

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