Abstract:

Introduction: Although the effective refractory period (ERP) is one of the main electrophysiological properties governing atrial tachycardia (AT) maintenance, ERP personalization is rarely performed when creating patient-specifi c computer models of the atria to inform clinical decision-making. State-of-the-art models usually do not consider physiological ERP gradients but assume a homogeneous ERP distribution. This assumption might have an influence on the ability to induce reentries in the model.Aim: To evaluate the impact of incorporating clinical ERP measurements when creating in silico personalized models to predict vulnerability to atrial fibrillation (AF).Methods: Clinical ERP measurements were obtained from three patients from multiple locations in the atria. The protocol for ERP identification consisted of trains of 7 S1 stimuli with a basic cycle length of 500ms followed by an S2 stimulus with a coupling interval between 300 and 200ms in decrements of 10ms until loss of capture. The atrial geometries from the electroanatomical mapping system were used to generate personalized atrial models. To reproduce patient-specific ERP, the established Courtemanche cellular model was gradually reparameterized from control conditions to a setup representing AF-induced remodeling. Three different approaches were studied:1) a control scenario with no ERP personalization 2) a discrete split where each region had a single ERP value and3) a continuous ERP distribution by interpolation of measured ERP data (Fig. 1). Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different locations separated by 3cm. The number and location of inducing points and type of arrhythmia were determined for the three approaches. The mean conduction velocity was setto 0.7 m/s and the electrical propagation in the atria was modeled by the monodomain equation and solved withopenCARP.Results: Incorporating patient-specific ERP as a continuous distribution did not induce any reentrant activity. A summary of induced ATs is shown in Table 1. For patient A, AF was induced from 3 different locations with the control setup, whereas 9 ATs were induced with the regional method, of which 4 were AF and 5 macro reentries. For patient B, AF was induced from 1 point with the control setup; whereas with the regional approach, AF was induced at 4 points. For patient C, only one macro reentry was induced with the regional method.Conclusion: The incorporation of patient-specifi c ERP values has an impact on the assessment of AF vulnerability. Furthermore, the type of personalization affects the likelihood of AF inducibility. The incorporation of more detailed ERP distributions may lead to a more accurate prediction of AF trigger points and could in the future inform patient-specifi c therapy planning. Larger cohorts need to follow to demonstrate the role of incorporating clinical patient-specifi c ERP values into personalized models for predicting AF vulnerability.

Abstract:

Dilated cardiomyopathy is a cardiac abnormality affecting millions of people worldwide which complications are due to the several modifications that occur in the structure and function of the heart. The numerous aspects that have to be considered make this disease extremely hard to deal with, especially from an in-vivo or even in-vitro examination. In that context, computational cardiology has become a crucial aspect of modern medicine, enabling the understanding of complex pathologies and favoring the scientific breakthrough. In this light, and in order to gain insight into dilated cardiomyopathy, a detailed computational model of a failing heart is presented in this work. Both, electrophysiology and mechanics were considered in a three-dimensional model of the whole heart, in which diverse features, such as structural modifications, fiber reorganization, cellular alterations and other variables characteristic of the disease, were adopted and assessed. The results obtained from analysing these variables individually and combined show that cellular alterations, especially those affecting the electrophysiology, are mainly responsible for the poor function of the heart in dilated cardiomyopathy, whereas multi-scale changes, such as fiber reorganization and cellular uncoupling, have little effect on the mechanical behaviour. The structural remodeling and modifications in the passive properties and circulatory system are key aspects to take into account, which together with the cellular remodeling constitute the features that need to be included in a computational model of dilated cardiomyopathy.