Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study
Financiación H2020 / H2020 Funds
Resumen: 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
Idioma: Inglés
DOI: 10.1016/j.morpho.2019.09.001
Año: 2019
Publicado en: Morphologie 103, 343 (2019), 169 - 179
ISSN: 1286-0115

Factor impacto SCIMAGO: 0.365 - Anatomy (Q3)

Financiación: info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


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