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

室性心律失常不良结构和功能重塑的电图特征

• 批准号: 10571137
• 负责人: Albert Joseph Rogers
• 金额: $ 17.17万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571137
• 关键词: Ablation; Acceleration; Action Potentials; Acute; Anti-Arrhythmia Agents; Area; Arrhythmia; Biomedical Engineering; Cardiac; Cardiac ablation; Cardiomyopathies; Cause of Death; Cell physiology; Cessation of life; Cicatrix; Classification; Classification Scheme; Clinical; Communication; Coronary Arteriosclerosis; Coronary artery; Data; Data Analyses; Data Science; Databases; Diagnosis; Disease; Electrophysiology (science); Engineering; Enrollment; Etiology; Fellowship; Fibrosis; Fingerprint; Foundations; Functional disorder; Future; Gadolinium; Goals; Growth; Heart; Heart Abnormalities; Heart Atrium; Heart failure; Image; Implantable Defibrillators; Intervention; Ischemia; Knowledge; Label; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medicine; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methods; Modeling; Myocardial; Myocardial Ischemia; Myocardium; National Heart, Lung, and Blood Institute; Network-based; Outcome; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiological; Prevention; Procedures; Recurrence; Refractory; Research; Research Infrastructure; Resources; Risk; Risk Assessment; Scientist; Signal Transduction; Structural defect; Sudden Death; Techniques; Therapeutic; Therapeutic Intervention; Time; Training; Translating; Translational Research; Translations; United States; United States National Institutes of Health; Ventricular; Ventricular Arrhythmia; Ventricular Fibrillation; Writing; arrhythmogenic cardiomyopathy; clinical decision-making; clinical training; clinical translation; density; imaging study; improved; in vivo; indexing; innovation; machine learning model; multimodal data; multimodality; neural network; novel; novel therapeutics; patient registry; patient response; patient stratification; porcine model; predict clinical outcome; programs; response; skills; spatial relationship; supportive environment; targeted treatment; tool; treatment response; voltage

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.

项目摘要 心肌病患者的心律不齐仍然是死亡的主要原因，并解释了 在美国，每年30万人死亡。但是，这些节奏的当前分类是基于 主要关于心肌病是否是由于冠状动脉阻塞而引起的，并且患者分层较差 对治疗，心律失常和病理生理学的反应。 该项目的目的是制定患者心室心律失常的可行分类方案 基于结构和电气异常之间的相互作用的心肌病 从每个患者的心脏中测量。这种基于病理生理学测量的分类将 告知风险评估，介入疗法和药物的临床方法。 该提案概述了三个具体目标：1）识别心内膜中心的电气指纹 使用机器学习接受训练的心内膜高密度接触电子的机器学习 来自我们大型患者的区域延迟的磁共振成像的区域延迟纤维化的地面真相 注册表。 2）制定和验证可以在临床电生理学上使用的映射策略 测量心室难治期，一种电气重塑的度量，表明可以维持VA的能力， 从猪模型的心脏学习高密度电信号的机器学习，该猪模型通过重圆度标记 黄金标准的指数，简单地记录了单相动作电位。 3）衍生小说 基于纤维化区域分布的VA患者心律失常心肌病的表型 和电重塑，并将其与在井中的消融和复发相关联 表征患者注册表。 为了成功完成拟议项目，培训对象包括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）翻译介入 程序和4）翻译临床电生理学。拟议的NHLBI K23奖将提供 保护候选人获得此高级培训的时间，通过书面和 口语交流，并为重点的独立研究计划奠定基础 在既定的环境中 涉及工程和医学的识别人，合作者和跨学科专家。