Artificial intelligence analysis of atrial remodeling evolution in patients with atrial fibrillation: Towards optimal ablation strategies

心房颤动患者心房重塑演变的人工智能分析：寻求最佳消融策略

• 批准号: 10559270
• 负责人: Eugene Kholmovski
• 金额: $ 79.86万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-20 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559270
• 关键词: Ablation; American; Arrhythmia; Artificial Intelligence; Atrial Fibrillation; Cardiac ablation; Catheters; Clinical; Clinical Data; Computer Models; Development; Diagnosis; Disease; Electrophysiology (science); Environment; Evolution; Fibrosis; Health Care Costs; Heart Atrium; Image; Knowledge acquisition; Lead; Learning; Link; Longitudinal Studies; Machine Learning; Maps; Medical; Modeling; Morbidity - disease rate; Morphology; Patients; Pharmaceutical Preparations; Population; Prevention; Procedures; Psychological reinforcement; Public Health; Pulmonary veins; Recommendation; Recurrence; Refractory; Research; Role; Shapes; Structure; Testing; Time; Treatment Efficacy; cardiac magnetic resonance imaging; clinically significant; contrast enhanced; deep learning; deep reinforcement learning; economic impact; functional disability; global health; health goals; heart rhythm; improved; indexing; individual patient; insight; mortality; novel; novel strategies; patient stratification; personalized strategies; prospective; risk minimization; stem; success; treatment optimization

PROJECT SUMMARY Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, leading to morbidity and mortality in 1-2% of the population and contributing significantly to global health care costs. For patients in whom AF can- not be treated by drugs, the recommended therapy is catheter-based ablation to isolate arrhythmia triggers and eliminate the substrate for arrhythmia perpetuation. The success rate of catheter ablation in rhythm controlled AF patients is 50-75%, and is worse in patients with persistent AF. The mechanisms by which baseline and post-ablation atrial remodeling, including atrial distension, functional impairment, and fibrosis, contribute to AF recurrence following catheter ablation, are not well understood and the underling factors have not been charac- terized. Understanding atrial remodeling in drug-refractory AF patients and discovering new personalized strategies for successful AF ablation and prevention of AF recurrence is a quest of paramount clinical significance. There is an urgent need to develop new approaches to ablation that account mechanistically for the remodeling of the atrial substrate post-procedure, and thereby improve the efficacy of the therapy and eliminate repeat procedures. The overall objective of this application is to use novel combination of imaging, artificial intelligence (AI), electroanatomical mapping, and mechanistic computational modeling to understand the causes for AF recurrence in drug-refractory AF patients and to develop a new paradigm for personalized ablation that eliminates repeat procedures. Leveraging our advancements in the acquisition of high-quality atrial im- ages, our expertise in AI and particularly deep learning, and our ability to efficiently generate personalized com- putational atrial models, we propose to characterize baseline atrial remodeling in shape, structure and function as well as its progression post-procedure. Using the obtained insights, we will develop a comprehensive abla- tion strategy where AF ablation targets will be determined by reinforcement learning based on the mechanistic knowledge acquired in the proposed studies. The project will culminate in a pilot prospective patient study that will test the new ablation strategy. Successful execution of the project will pave the way for a paradigm shift in the clinical procedure of AF ablation and in the quest to eliminate repeat procedures in drug-refractory AF patients, resulting in a dramatic improvement in the efficacy of the therapy. Importantly, completion of this project will be major leap forward in the integration of imaging, AI, and computational modeling in the diagnosis and treatment of heart rhythm disorders.

项目摘要 房颤（AF）是最常见的持续性心律失常，导致患者的发病率和死亡率。 占人口的1 - 2%，并对全球医疗保健成本做出重大贡献。对于房颤患者- 不能用药物治疗，推荐的治疗方法是基于导管的消融术，以隔离心律失常触发因素， 消除心律失常持续存在的基础。心律控制下导管消融术的成功率 AF患者中，50 - 75%的患者发生房颤，持续性AF患者的情况更糟。 消融后心房重构，包括心房扩张、功能损害和纤维化，可导致AF 导管消融术后复发，还没有得到很好的理解，基础因素还没有被描述。 化。了解药物难治性房颤患者的心房重构并发现新的个性化治疗方案 成功的AF消融和预防AF复发的策略是临床上最重要的探索。 重要性。迫切需要开发新的消融方法，从机制上解释 术后心房基质的重塑，从而提高治疗的有效性并消除 重复程序。 本申请的总体目标是使用成像、阿尔蒂智能、 (AI)电解剖标测和机械计算建模，以了解 药物难治性房颤患者的房颤复发，并开发个性化消融的新范例 避免了重复手术。利用我们在获取高质量心房肌方面的进步， 年龄，我们在人工智能，特别是深度学习方面的专业知识，以及我们有效生成个性化内容的能力， 假设心房模型，我们建议在形状，结构和功能上表征基线心房重构 以及术后的进展。利用所获得的见解，我们将制定一个全面的ABLA- AF消融目标将通过基于机制的强化学习来确定的策略 在拟议的研究中获得的知识。该项目将在一项试点前瞻性患者研究中达到高潮， 将测试新的消融策略该项目的成功执行将为全球化的范式转变铺平道路。 AF消融的临床手术以及在寻求消除药物难治性AF患者的重复手术中， 导致治疗效果的显著改善。重要的是，该项目的完成将是 在诊断和治疗中整合成像、AI和计算建模的重大飞跃 心脏节律紊乱