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

Area of Science:

• Cardiology
• Genomics
• Medical Imaging
• Machine Learning

Background:

• Heart failure with preserved ejection fraction (HFpEF) has high morbidity and mortality, with limited effective treatments.
• Existing therapies are less effective in HFpEF compared to HF with reduced ejection fraction (HFrEF).
• HFpEF likely comprises distinct patient subtypes requiring tailored therapeutic strategies.

Purpose of the Study:

• To develop a comprehensive approach for subtyping patients with HFpEF.
• To identify distinct HFpEF subtypes using advanced analytical methods.
• To lay the groundwork for personalized treatment strategies in HFpEF.

Main Methods:

• Prospective, multicenter observational cohort study of 500 HFpEF patients, 50 HFrEF patients, and 50 controls.
• Integration of clinical data with genomic, transcriptomic, metabolomic, echocardiography, and magnetic resonance imaging (MRI).
• Application of machine learning for multimodal clustering to identify and validate HFpEF subtypes.

Main Results:

• The study protocol is detailed, outlining the comprehensive data collection and analysis plan.
• Machine learning will be used to cluster patients based on multimodal data.
• The primary outcome is a composite of 1-year cardiovascular mortality or rehospitalization.

Conclusions:

• Integrating advanced imaging (MRI) with multi-omics data (genomics, metabolomics) provides a holistic view of HFpEF.
• This approach will aid in distinguishing functional HFpEF subgroups.
• Findings may form the basis for developing personalized therapies for HFpEF patients.