Computational detection of electrophysiological abnormalities in hypertrophic cardiomyopathy for risk stratification

Hypertrophic cardiomyopathy (HCM) is a common cardiac genetic disease and a major cause of sudden cardiac death (SCD) in young adults. While most patients remain asymptomatic, others suffer from SCD triggered by ventricular arrhythmias. An accurate detection of these high-risk patients in order to provide them with appropriate treatment is therefore essential, and it remains a challenge as current electrocardiogram (ECG) biomarkers are not specific. In this thesis, we develop computational methods for the analysis and interpretation of electrophysiological clinical data in order to investigate the diversity of HCM phenotypes with the ultimate aim to improve risk stratification in HCM. First, by combining computational clustering and mathematical modelling, we identify four distinct ECG phenotypes exhibiting differences in hypertrophy distribution and risk of arrhythmia. The group with primary repolarization abnormalities and coexistence of apical and septal hypertrophy shows a higher HCM Risk-SCD score compared to other groups. Second, we explore the influence of structural and electrophysiological mechanisms on the ECG to explain the four HCM phenotypes identified, by using a whole-body personalized 3D computer simulation framework. We show that apico-basal repolarization heterogeneities explain the T wave inversions in the high risk group, and that an abnormal Purkinje system may explain the QRS abnormalities in another group. Finally, we further investigate the presence of repolarization biomarkers in HCM, specifically action potential alternans, and show that HCM cardiomyocytes do not overall exhibit more action potential alternans compared to controls, which may suggest other whole-organ mechanisms responsible for T wave alternans. Overall, this thesis contributes towards a better understanding of HCM phenotypic heterogeneity and the improvement of individual patient management.