Evidence map›Paper›PMID 35361446›Full record

ArticleJACC. Heart failure2022

Frailty, Guideline-Directed Medical Therapy, and Outcomes in HFrEF: From the GUIDE-IT Trial.

Muhammad Shahzeb Khan, Matthew W Segar, Muhammad Shariq Usman, Sumitabh Singh, Stephen J Greene, Gregg C Fonarow, Stefan D Anker, G Michael Felker, James L Januzzi, Javed Butler and 1 more

Open access · greenAbstract read
In one paragraph

Article in JACC. Heart failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 4 pooled it
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 57 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Muhammad Shahzeb KhanDivision of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA.
Matthew W SegarDepartment of Cardiology, Texas Heart Institute, Houston, Texas, USA.
Muhammad Shariq UsmanDepartment of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA.
Sumitabh SinghDivision of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Stephen J GreeneDivision of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
Gregg C FonarowDivision of Cardiology, Ronald Reagan-UCLA Medical Center, Los Angeles, California, USA.
Stefan D AnkerDepartment of Cardiology (CVK) and Berlin Institute of Health Center for Regenerative Therapies (BCRT); German Center for Cardiovascular Research (DZHK), Charité Universitäts Medizin Berlin, Germany.
G Michael FelkerDivision of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
James L JanuzziDivision of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts; Baim Institute for Clinical Research, Boston, Massachusetts, USA.
Javed ButlerDepartment of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA.
Ambarish PandeyDivision of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA. Electronic address: ambarish.pandey@utsouthwestern.edu.
University of Mississippi Medical Center · USDuke University · USRonald Reagan UCLA Medical Center · USThe University of Texas Southwestern Medical Center · US

Funding

GUIDing Evidence Based Therapy Using Biomarker Intensified Treatment (CCC)R01HL105448 · NHLBI · DUKE UNIVERSITY · PI FELKER, GARY M · 2012 to 2016
$12.8M
GUIDing Evidence Based Therapy Using Biomarker Intensified Treatment (DCC)R01HL105451 · NHLBI · DUKE UNIVERSITY · PI ANSTROM, KEVIN J · 2012 to 2016
$3.5M
Polypill strategy for the evidence-based management of heart failure with reduced ejection fraction in an underserved patient populationR01MD017529 · NIMHD · UT SOUTHWESTERN MEDICAL CENTER · PI PANDEY, AMBARISH, WANG, THOMAS J. · 2022 to 2025
$2.9M
Guiding Evidence Based Therapy Using Biomarker Intensified Treatment (EQOL)R01HL105457 · NHLBI · DUKE UNIVERSITY · PI MARK, DANIEL B · 2012 to 2016
$1.1M
Evaluation of Racial Differences in Cardiorespiratory Fitness Decline with Aging & Underlying Biological MechanismsR03AG067960 · NIA · UT SOUTHWESTERN MEDICAL CENTER · PI PANDEY, AMBARISH · 2020 to 2021
$327k
NHLBI NIH HHS R01 HL105448NHLBI NIH HHS R01 HL105451NHLBI NIH HHS R01 HL105457NIA NIH HHS R03 AG067960NIMHD NIH HHS R01 MD017529
6 · The paper itself

Abstract

objectivesIn this study, we sought to evaluate the association of frailty with the use of optimal guideline-directed medical therapy (GDMT) and outcomes in heart failure with reduced ejection fraction (HFrEF).

backgroundThe burden of frailty in HFrEF is high, and the patterns of GDMT use according to frailty status have not been studied previously.

methodsA post hoc analysis of patients with HFrEF enrolled in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) trial was conducted. Frailty was assessed with the use of a frailty index (FI) using a 38-variable deficit model, and participants were categorized into 3 groups: class 1: nonfrail, FI <0.21); class 2: intermediate frailty, FI 0.21-0.31), and class 3: high frailty, FI >0.31). Multivariate-adjusted Cox models were used to study the association of frailty status with clinical outcomes. Use of optimal GDMT over time (beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and mineralocorticoid receptor antagonists) across frailty strata was assessed with the use of adjusted linear and logistic mixed-effect models.

resultsThe study included 879 participants, of which 56.3% had high frailty burden (class 3 FI). A higher frailty burden was associated with a significantly higher risk of HF hospitalization or death in adjusted Cox models: high frailty vs nonfrail HR: 1.76, 95% CI: 1.20-2.58. On follow-up, participants with high frailty burden also had a significantly lower likelihood of achieving optimal GDMT: high frailty vs non-frail GDMT triple therapy use at study end: 17.7% vs 28.4%; P interaction, frailty class × time <0.001.

conclusionsPatients with HFrEF with a high burden of frailty have a significantly higher risk for adverse clinical outcomes and are less likely to be initiated and up-titrated on an optimal GDMT regimen.

Indexed as

FrailtyHeart FailureAngiotensin-Converting Enzyme InhibitorsHumansMineralocorticoid Receptor AntagonistsStroke VolumeAngiotensin-Converting Enzyme InhibitorsMineralocorticoid Receptor Antagonistsfrailtyguideline-directed medical therapyheart failure with reduced ejection fractionmortality

Identifiers

PMID35361446
PMCPMC10539014
OpenAlexW4220923527

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.