Evidence map›Paper›PMID 41378481›Full record

ArticleJournal of the American Heart Association2025

Study Protocol for HeartMagic: A Prospective Observational Cohort Characterizing Subtypes of Heart Failure With Preserved Ejection Fraction.

Philippe Meyer, Angela Rocca, Jaume Banus, Augustin C Ogier, Costa Georgantas, Pauline Calarnou, Anam Fatima, Jean-Paul Vallée, Jean-François Deux, Aurélien Thomas and 11 more

Abstract readClinical Trial Protocol
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Transformer networks enable fast and robust dictionary generation for multiparametric cardiac mapping with variable timing.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026
    Article
  2. Article
  3. Cardiac function assessment with deep-learning-based automatic segmentation of free-running four-dimensional whole-heart cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
    Article
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

21 authors.

Philippe MeyerCardiology Division University Hospital of Geneva Geneva Switzerland.ORCID 0000-0002-2430-8953
Angela RoccaCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0002-1634-6992
Jaume BanusDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.
Augustin C OgierDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.
Costa GeorgantasDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.ORCID 0009-0008-8891-4582
Pauline CalarnouDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.
Anam FatimaRadiology Division, Diagnostic Department Geneva University Hospitals and University of Geneva Geneva Switzerland.
Jean-Paul ValléeRadiology Division, Diagnostic Department Geneva University Hospitals and University of Geneva Geneva Switzerland.ORCID 0000-0001-7546-2013
Jean-François DeuxRadiology Division, Diagnostic Department Geneva University Hospitals and University of Geneva Geneva Switzerland.
Aurélien ThomasFaculty Unit of Toxicology, CURML, Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland.ORCID 0000-0001-6790-2285
Julien MarquisLausanne Genomic Technologies Facility, University of Lausanne Lausanne Switzerland.ORCID 0000-0002-2020-1534
Pierre MonneyCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0002-3083-7333
Henri LuCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0002-9074-6505
Alessandra Pia PorrettaCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0001-7950-8090
Jean-Baptiste LedouxDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.ORCID 0000-0003-0447-5073
Cloé TillierCardiology Division University Hospital of Geneva Geneva Switzerland.
Lindsey A CroweRadiology Division, Diagnostic Department Geneva University Hospitals and University of Geneva Geneva Switzerland.
Tamila AbdurashidovaCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0001-9560-6723
Jonas RichiardiDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.
Roger HullinCardiology Division Lausanne University Hospital (CHUV) Lausanne Switzerland.ORCID 0000-0002-5738-1903
Ruud B van HeeswijkDepartment of Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland.ORCID 0000-0001-5028-4521

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure (HF) is a life-threatening syndrome with significant morbidity and mortality. Although evidence-based drug treatments have effectively reduced morbidity and mortality in HF with reduced ejection fraction (EF), few therapies have been demonstrated to improve outcomes in HF with preserved EF. This may be caused by the existence of several HF with preserved EF subtypes that each need different treatments. There is therefore an unmet need for a comprehensive approach to subtype patients with HF with preserved EF. This protocol details the approach employed in the HeartMagic (Heart Failure Studied With a Machine Learning, Genomics, and Imaging Combination) study to address this gap.

methodsThis prospective multicenter observational cohort study will include 500 consecutive patients with HF with preserved EF at 2 Swiss university hospitals, along with 50 age-matched patients with HF with reduced EF and 50 healthy controls. In addition to routine clinical workup, participants undergo genomic, transcriptomic, and metabolomic analyses, and the anatomy, composition, and function of the heart are quantified by comprehensive echocardiography and magnetic resonance imaging. Quantitative magnetic resonance imaging is also applied to characterize the kidney. The primary outcome is a composite of 1-year cardiovascular mortality or rehospitalization. Machine learning-based multimodal clustering will be employed to identify distinct HF with preserved EF subtypes. Statistical analysis will include group comparisons, survival analysis, and integrative multimodal clustering combining clinical, imaging, ECG, genomic, transcriptomic, and metabolomic data to identify and validate HF with preserved EF subtypes.

conclusionsThe integration of comprehensive magnetic resonance imaging with extensive genomic and metabolomic profiling in this study will result in an unprecedented panoramic view of HF with preserved EF and help distinguish functional subgroups, which may provide a basis for personalized therapies.

Indexed as

Heart FailureStroke VolumeVentricular Function, LeftAgedEchocardiographyFemaleGenomicsHumansMachine LearningMagnetic Resonance ImagingMaleMiddle AgedMulticenter Studies as TopicObservational Studies as TopicProspective StudiesSwitzerlandechocardiographygenomicsheart failureHFpEFmachine learningmetabolomicsquantitative MRI

Identifiers

PMID41378481
PMCPMC12826941

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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.