000099440 001__ 99440
000099440 005__ 20240912131720.0
000099440 0247_ $$2doi$$a10.1016/j.morpho.2019.09.001
000099440 0248_ $$2sideral$$a122800
000099440 037__ $$aART-2019-122800
000099440 041__ $$aeng
000099440 100__ $$aLyon, Aurore
000099440 245__ $$aImproving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study
000099440 260__ $$c2019
000099440 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099440 5203_ $$aMost 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
000099440 536__ $$9info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675451-CompBioMed
000099440 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099440 592__ $$a0.365$$b2019
000099440 593__ $$aAnatomy$$c2019$$dQ3
000099440 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099440 700__ $$0(orcid)0000-0003-3183-4107$$aMincholé, Ana$$uUniversidad de Zaragoza
000099440 700__ $$aBueno-Orovio, Alfonso
000099440 700__ $$aRodriguez, Blanca
000099440 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000099440 773__ $$g103, 343 (2019), 169 - 179$$tMorphologie$$x1286-0115
000099440 8564_ $$s4522010$$uhttps://zaguan.unizar.es/record/99440/files/texto_completo.pdf$$yVersión publicada
000099440 8564_ $$s2236770$$uhttps://zaguan.unizar.es/record/99440/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099440 909CO $$ooai:zaguan.unizar.es:99440$$particulos$$pdriver
000099440 951__ $$a2024-09-12-13:15:37
000099440 980__ $$aARTICLE