Mechanisms of fibrosis and remodeling in arrhythmogenic mitral valve prolapse

About This Grant

PROJECT SUMMARY/ABSTRACT Mitral valve prolapse (MVP) is a common valvular disease, affecting 2-4% of the population. It is increasingly linked with life-threatening complex ventricular arrhythmias (CVA) and sudden cardiac death (SCD), even in the absence of significant mitral regurgitation. Despite this emerging association, the mechanisms underlying arrhythmogenesis remain poorly understood, limiting risk stratification and treatment strategies. New tools are needed to allow clinicians to identify patients at risk of devastating cardiac events from in vivo measurements of left ventricle (LV) function. This proposal aims to address the current gap in mechanistic understanding and clinical risk-assessment through the development of “personalized” computational cardiac models from in vivo imaging data. We hypothesize that stress-induced fibrosis in the myocardium is a substrate for altered mechanics, hemodynamics, and electrical disturbance in the LV; these patient-specific models can elucidate the relationship between these events. Previous work in our lab has leveraged subject-specific models of the heart to investigate the effects of biomechanical remodeling of cardiac function. We propose to use a similar framework to model the human heart and deepen our understanding of the biomechanical remodeling in MVP-associated arrhythmias. Building on these insights, we aim to elucidate image-derived biomarkers for the non-invasive prediction of arrhythmias in MVP patients. We will leverage multimodal imaging data - including fibrosis quantification, deformation measures, and flow dynamics - to predict arrhythmic risk and identify patients who may benefit from early intervention. Specifically, we intend to achieve the following aims: Determine the biomechanical mechanisms underlying fibrosis in arrhythmia MVP. We will use state-of-the-art in vivo imaging, image processing, and patient-specific 3-D computational modeling tools to characterize the biomechanical remodeling of the LV in MVP patients and link these events to arrhythmogenesis. Identify high-fidelity intracardiac hemodynamic characteristics of MVP patients not captured by traditional organ-level metrics. We will use established computational fluid dynamics (CFD) techniques to assess the patient-specific intraventricular hemodynamics in the LV of MVP patients and correlate altered flow with arrhythmia. We will integrate imaging-based biomechanical and hemodynamic biomarkers to reveal the predictive power of image-derived metrics to enable early identification of high-risk patients and guide personalized clinical management and intervention strategies.