Evidence map›Paper›PMID 35767073›Full record

ReviewHerz2022

The future of heart failure with preserved ejection fraction : Deep phenotyping for targeted therapeutics.

Frank R Heinzel, Sanjiv J Shah

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Herz, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 5% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Frank R HeinzelMedizinische Klinik mit Schwerpunkt Kardiologie, Charité - Universitätsmedizin, Campus Virchow-Klinikum, Berlin, Germany. frank.heinzel@charite.de.
Sanjiv J ShahDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Charité - Universitätsmedizin Berlin · DENorthwestern University · US

Funding

HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targetsU54HL160273 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Abel N. Kho, Yuan Luo · 2021 to 2026
$19.0M
Transition from Risk Factors to Early HF: Prevalence, Pathogenesis, and PhenomicsR01HL127028 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI BERTONI, ALAIN GERALD, SHAH, SANJIV J · 2015 to 2018
$6.5M
Study of Cardiac Mechanics in Systemic HypertensionR01HL107577 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SHAH, SANJIV J · 2011 to 2019
$4.1M
Machine learning for the automated identification and tracking of rare myocardial diseasesR01HL140731 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MACRAE, CALUM A., SHAH, SANJIV J · 2018 to 2021
$2.8M
Precision Medicine for Dilated Cardiomyopathy—Novel Assessment of Cardiac Mechanics via Speckle Tracking Echocardiography to Identify Early PhenotypesR01HL149423 · NHLBI · OHIO STATE UNIVERSITY · PI HERSHBERGER, RAY E., KINNAMON, DANIEL DAVIS · 2019 to 2022
$1.6M
NHLBI NIH HHS R01 HL107577NHLBI NIH HHS R01 HL127028NHLBI NIH HHS R01 HL140731NHLBI NIH HHS R01 HL149423NHLBI NIH HHS U54 HL160273
6 · The paper itself

Abstract

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a "one-size-fits-all" approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.

Indexed as

Heart FailureHemodynamicsHumansPhenotypeStroke VolumeSyndromeVentricular Function, LeftVentricular RemodelingCardiovascular diseaseClassificationMachine learningPhenomappingPhenotype

Identifiers

PMID35767073
PMCPMC9244058
OpenAlexW4283723618

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.