ArticleJournal of the American Heart Association2025
Study Protocol for HeartMagic: A Prospective Observational Cohort Characterizing Subtypes of Heart Failure With Preserved Ejection Fraction.
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.
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3 citing papers in PubMed.
- 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 · 2026Article
- Construction of a predictive model for the efficacy of enhanced external counterpulsation therapy in patients with heart failure with preserved ejection fraction based on automated machine learning and echocardiography.Frontiers in cardiovascular medicine · 2026Article
- 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 ResonanceArticle
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21 authors.
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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.
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