A modelling and imaging approach to further understanding of pathophysiology in arrhythmogenic cardiomyopathy

Background: Arrhythmogenic Cardiomyopathy (ACM) is a potentially lethal form of cardiomyopathy, with a high incidence of mortality. It is prominently characterised by abnormal ventricular anatomy, Fbrofatty replacement of the myocardium and the His-Purkinje system, as well as electrophysiological remodelling. It remains unknown how the broad spectrum of ACM-related co-morbidities alter the 12 lead electrogram (ECG). Ventricular activation abnormalities result in QRS complex and terminal activation duration prologation, as well as abnormal R-wave progression and low amplitude signals are diagnosis criteria of ACM on the 12-lead ECG. The pathological remodelling leading to the above electrocardiographic findings can be of electrical, geometrical or structural nature or a combination of substrates. Diagnosis of this clinical entity is challenging and relies on a combination of ECG, non-invasive imaging approaches to access myocardial muscle structrure and geometry as well as genetic testing. Despite the multi-modality diagnosis approach, misdiagnosis is common and some patients are not diagnosed until post-mortem examination is performed. Improvements in diagnosis are much needed and one challenge is understanding the link between ECG manifestations and underlying disease burden. Furthermore, no ACM specific treatments exist to date and life quality of sufferers is greatly reduced as cardiac defibrillators and guidelines to avoid physical activity are the treament norm. Obtaining an accurate diagnosis non-invasively can contribute to better risk stratification and patient management, ultimately improving the life quality and safety of patients.
Aims: It is known that several mechanisms can lead to slow electrical conduction in the ventricles of ACM patients. Plausible single cell electrophysiological mechanisms, such as co-modulation of INa - I K1 ionic channels, leading to tissue level electrical conduction velocity modulation in ACM are explored. This thesis aims to uncover which mechanisms of conduction slowing prevail in patients with ACM by integrating information from imaging data and performing personalised activation sequence simulations of patients diagnosed with ACM. The scope is to also relate the different conduction abnormalities to signature traits on the ECG, so as to help refine diagnosis and disease burden non-invasively.
Methods: The effect of ACM specific ionic channel joint reduction of conduction is explored via a study relating co-modulation in key ionic channels ( INa - IK1 ), known to be remodelled in ACM variants and the tissue level electrical conduction velocity in 2D tissue simulations. Personalised models of electrical conduction are created from Magnetic Resonance Imaging (MRI) data of a cohort of ACM patients with 15 definite, 3 borderline and 2 plausible patients according to the 2010 international diagnosis guidelines. Patient ventricular and torso anatomies are extracted from the MRI images and regional fibrotic scar information is extracted from contrast agent enhanced MRI imaging. Different types of remodelling were introduced into graph-based models of sinus rhythm activation, namely localised fibrosis-mediated conduction slowing in the myocardial muscle or in the Purkinje-myocardial fast conduction system, myocardial tissue uncoupling leading to global ventricular tissue conduction slowing, right ventricular (RV) muscle disappearance leading to slowed conduction localised in the RV, RV muscle volume dilation and RV wall thinning. Insights from slab tissue simulations were used to estimate the order of magnitude of conduction velocity modulation due to ionic remodelling, clinical data (contrast enhanced MRI) for scar based conduction anomalies and from other computational studies quantifying conduction velocity modulation in the ACM myocardium were used to find physiological parameter intervals for the computational study. The effects of the above types of remodelling on features of the ECG relating to activation dynamics were quantified.
Results: Slab tissue simulations revealed that ACM-specific ionic remodelling of the INa - IK1 macromolecular complex can result in up to 45% bulk myocardial tissue CV modulation. A computational pipeline was adapted to access the effect of ACM ventricular substrates on conduction properties of the ventricles. The personalised data-driven models of two ACM patients with varying disease burden were used to successfully explain how the underlying whole ventricular remodelling impacted 6the clinical patients' ECG recordings. Computer simulations demonstrate that, in ACM, mild QRS prolongation (>100 ms), in absence of other QRS abnormalities, is explained by regional myocardial conduction slowing in the areas of MRI-identified fibrosis. However, MRI-segmented myocardial fibrosis and RV dilation alone proved insufficient to recapitulate more severe QRS abnormalities, such as severely prolonged mean QRS duration (>120 ms) across precordial leads, mean terminal activation dura- tion across V1-V4 (>55 ms), and low QRS mean R-wave amplitude (<2 mV) in precordial leads, as well as poor R-wave progression across precordial leads. A 70% decrease in Purkinje-endocardial conduction and 30% RV wall thinning were additionally required to explain these more severe abnormalities.

Keywords: acm
fibrosis
eikonal model
personalised simulations
ecg
arvc
Advisor: Bueno Orovio, A
Rodriguez Lopez, B
Zacur, E