Electrical Mapping Signatures of Adverse Structural and Functional Remodeling in Ventricular Arrhythmia

项目总结

PROJECT SUMMARY Ventricular arrhythmias remain the leading cause of death in patients with cardiomyopathy and account for up to 300,000 deaths per year in the United States. However, the current classifications of these rhythms is based largely on whether the cardiomyopathy is due to obstructed coronary arteries and poorly stratifies patient response to therapy, arrhythmic risk, and pathophysiology. The goal of this project is to develop an actionable classification scheme for ventricular arrhythmias in patients with cardiomyopathy that is based on the interplay between both structural and electrical abnormalities measured from each patient’s heart. Such a classification, based in measurements of pathophysiology, would inform the clinical approach to risk assessment, interventional therapies, and medications. The proposal outlines three Specific Aims: 1) To identify electrical fingerprints of endocardial, mid-myocardial and epicardial scar using machine learning of endocardial high-density contact electrograms trained to the ground truth of regional delayed gadolinium fibrosis on magnetic resonance imaging, from our large patient registry. 2) To develop and validate a mapping strategy that could be used at clinical electrophysiology to measure ventricular refractory period, a measure of electrical remodeling that indicates ability to sustain VA, by machine learning of high density electrical signals from the heart of a porcine model labeled by repolarization indices from the gold standard, simultaneously recorded, monophasic action potentials. And 3) To derive novel phenotypes of arrhythmogenic cardiomyopathy in patients with VA based on regional distributions of fibrosis and electrical remodeling, and associate these with acute response to ablation and recurrence in a well- characterized patient registry. To successfully complete the proposed project, training objectives include 1) advanced MRI processing and segmentation, 2) machine learning models for multimodal data analysis, 3) translational interventional procedures, and 4) translational clinical electrophysiology. The proposed NHLBI K23 award will provide protected time for the candidate to obtain this advanced training, to disseminate new knowledge via written and spoken communication, and to build the foundation for an independent research program focused on ventricular arrhythmia diagnosis, prevention, and therapy in a supportive environment of established mentorship, collaborators, and interdisciplinary experts spanning engineering and medicine.