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

项目总结 室性心律失常仍然是心肌病患者的主要死亡原因，并占UP 在美国每年有30万人死亡。然而，目前对这些节奏的分类是基于 这在很大程度上取决于心肌病是否是由于冠状动脉阻塞和患者分层不良所致 治疗反应、心律失常风险和病理生理学。 该项目的目标是开发一种可操作的室性心律失常分类方案。 心肌病是基于结构异常和电异常之间的相互作用 从每个病人的心脏测量。这种基于病理生理学测量的分类将 告知风险评估、介入治疗和药物治疗的临床方法。 该提案概述了三个具体目标：1)识别心内膜、心肌中部的电指纹 利用机器学习对心外膜瘢痕和心内膜高密度接触电信号进行训练 来自我们的大病人的磁共振成像显示的区域性延迟性Gd纤维化的基本事实 注册表。2)开发和验证可用于临床电生理学的标测策略 测量心室不应期，这是一种衡量电重构的指标，表明有能力维持VA，通过 复极标记猪模型心脏高密度电信号的机器学习 指标来自金本位，同时记录，单相动作电位。3)衍生小说 基于纤维化区域分布的VA患者致心律失常心肌病的表型 和电重构，并将这些与对消融和井中复发的急性反应联系在一起- 以病人登记为特征。 为了成功完成拟议的项目，培训目标包括1)先进的磁共振成像处理和 分段，2)用于多模式数据分析的机器学习模型，3)平移干预 程序和4)翻译临床电生理学。拟议的NHLBI K23奖项将提供 候选人获得高级培训、通过书面和书面形式传播新知识的保护时间 口语交流，并为专注于以下方面的独立研究计划奠定基础 在已建立的支持性环境中诊断、预防和治疗室性心律失常 导师、合作者和跨越工程学和医学的跨学科专家。

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.