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Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understanding of the mechanisms leading to arrhythmia, are crucial to target high-risk patients and provide them with appropriate treatment. However, this currently remains a challenge. In this paper, we present a successful example of implementing computational techniques for clinically-relevant applications. By combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in HCM, with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation. This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.

Original publication

DOI

10.1016/j.morpho.2019.09.001

Type

Journal article

Journal

Morphologie

Publication Date

27/09/2019

Keywords

Cardiac magnetic resonance imaging, Computational clustering, Computer modeling, Electrocardiography, Hypertrophic cardiomyopathy, Personalized simulations, Phenotyping, VPH, e-cardiology