TY - JOUR
T1 - Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations
T2 - A case study
AU - Lyon, A
AU - Mincholé, A
AU - Bueno-Orovio, A
AU - Rodriguez, B
N1 - Copyright © 2019 The Authors. Published by Elsevier Masson SAS.. All rights reserved.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
U2 - 10.1016/j.morpho.2019.09.001
DO - 10.1016/j.morpho.2019.09.001
M3 - Article
C2 - 31570308
SN - 1286-0115
VL - 103
SP - 169
EP - 179
JO - Morphologie
JF - Morphologie
IS - 343
ER -