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

项目摘要 室性心律失常仍然是心肌病患者死亡的主要原因， 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 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佳 对治疗的反应、药物风险和病理生理学。 该项目的目标是为患者的室性心律失常制定一个可操作的分类方案 基于结构和电异常之间相互作用的心肌病 测量每个病人的心脏。这种基于病理生理学测量的分类， 告知风险评估、介入治疗和药物的临床方法。 该提案概述了三个具体目标：1）识别内膜、中层心肌 和心外膜疤痕使用机器学习的心内膜高密度接触电描记图训练， 磁共振成像上的局部迟发性钆纤维化的基础事实，来自我们的大型患者 注册表2)开发并验证可用于临床电生理学的标测策略， 测量心室不应期，这是一种电重构指标，表明维持VA的能力， 通过复极标记的来自猪模型心脏的高密度电信号的机器学习 来自金标准的指数，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患者的致瘤性心肌病表型 和电重构，并将这些与对消融的急性反应和良好的复发相关联- 患者登记处特征。 为了成功完成拟议的项目，培训目标包括：1）先进的MRI处理， 分割，2）用于多模态数据分析的机器学习模型，3）平移介入 程序，和4）转化临床电生理学。拟议的NHLBI K23奖将提供 保护候选人获得这种高级培训的时间，通过书面和书面形式传播新知识， 口语交流，并建立一个独立的研究计划的基础，重点是 室性心律失常的诊断、预防和治疗， 导师，合作者和跨学科的专家跨越工程和医学。

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.