99440 20240912131720.0 doi 10.1016/j.morpho.2019.09.001 sideral 122800 ART-2019-122800 eng Lyon, Aurore Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study 2019 Access copy available to the general public Unrestricted 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 info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675451-CompBioMed info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.365 2019 Anatomy 2019 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Mincholé, Ana Universidad de Zaragoza (orcid)0000-0003-3183-4107 Bueno-Orovio, Alfonso Rodriguez, Blanca 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 103, 343 (2019), 169 - 179 Morphologie 1286-0115 4522010 http://zaguan.unizar.es/record/99440/files/texto_completo.pdf Versión publicada 2236770 http://zaguan.unizar.es/record/99440/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99440 articulos driver 2024-09-12-13:15:37 ARTICLE