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

项目概要 室性心律失常仍然是心肌病患者死亡的主要原因，占 美国每年有 30 万人死亡。然而，目前这些节律的分类是基于 主要取决于心肌病是否是由于冠状动脉阻塞造成的，并且对患者进行了不良分层 对治疗的反应、心律失常风险和病理生理学。 该项目的目标是为患者室性心律失常制定可行的分类方案 基于结构异常和电异常之间相互作用的心肌病 从每个患者的心脏测量。这种基于病理生理学测量的分类将 为风险评估、介入治疗和药物治疗的临床方法提供信息。 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 和心外膜疤痕，使用经过训练的心内膜高密度接触电图的机器学习 来自我们的大型患者的磁共振成像显示的区域迟发性钆纤维化的基本事实 注册表。 2) 开发和验证可用于临床电生理学的映射策略 测量心室不应期，这是一种电重塑的测量方法，表明维持 VA 的能力，通过 对来自猪模型心脏的高密度电信号进行机器学习，标记为复极化 来自黄金标准的指数，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 患者致心律失常性心肌病表型 和电重塑，并将这些与良好的消融和复发的急性反应联系起来 特征化的患者登记。 为了成功完成拟议项目，培训目标包括 1) 高级 MRI 处理和 分割，2) 用于多模式数据分析的机器学习模型，3) 转化介入 程序，以及 4) 转化临床电生理学。拟议的 NHLBI K23 奖项将提供 为候选人提供受保护的时间来获得这种高级培训，通过书面和传播新知识 口语交流，并为专注于以下方面的独立研究项目奠定基础 在已建立的支持性环境中进行室性心律失常的诊断、预防和治疗 导师、合作者以及工程和医学领域的跨学科专家。

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.