Simulation Guidance of Ablation Therapy for Persistent Atrial Fibrillation

持续性心房颤动消融治疗模拟指导

• 批准号: 10000128
• 负责人: HUGH G CALKINS
• 金额: $ 76.94万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10000128
• 关键词: Ablation; Address; American; Animal Model; Arrhythmia; Atrial Fibrillation; Behavior; Behavioral; Biological; Canis familiaris; Cardiac ablation; Catheters; Cells; Clinical; Clinical Research; Computer Models; Diagnosis; Electrophysiology (science); Ensure; Environment; Fibrosis; Freedom; Gadolinium; Geometry; Health Care Costs; Healthcare; Heart Atrium; Heart Diseases; Hemorrhage; Histology; Hour; Human; Image; Individual; Lesion; MRI Scans; Magnetic Resonance Imaging; Measurement; Medical Economics; Methodology; Modeling; Molecular; Morbidity - disease rate; Nature; Navigation System; Organ; Outcome; Patient Simulation; Patients; Pharmaceutical Preparations; Pilot Projects; Population; Procedures; Radiation; Recording of previous events; Recurrence; Risk; Site; Structure; Testing; Tissues; Treatment Efficacy; advanced simulation; base; clinically significant; contrast enhanced; economic impact; effective therapy; experimental study; global health; improved; in silico; mortality; multi-scale modeling; novel; novel strategies; personalized predictions; prospective; response; simulation; success; treatment optimization

PROJECT SUMMARY This application is in response to PAR-15-085: Predictive Multiscale Models for Biomedical, Biological, Behavioral, Environmental and Clinical Research (Interagency U01), with Cutting Edge Challenge: Predictive multiscale models to improve clinical workflow, standard operating procedures, patient-specific modeling for diagnosis and therapy planning. 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. However, outcomes of the procedure are poor ( 50% suc- cess rate) in patients with persistent AF (PsAF) due to the presence of extensive atrial fibrosis, which confounds ablation strategies. There is an urgent need for new approaches that can result in swift and accurate iden- tification of optimal ablation targets for PsAF and thereby improve the efficacy of and increase the tolerance for the therapy, as well as reduce post-procedure complications and repeated ablations. The overall objective of this application is to develop and validate a novel personalized multiscale modeling strategy for determining the optimal targets for catheter ablation of the fibrotic substrate in patients with PsAF. We propose to develop and validate atrial models reconstructed from MRI images of pa- tients with PsAF and fibrotic remodeling. The models will integrate mechanistically functions from the molecular level to the electrophysiological interactions in the intact organ. We will parametrize and validate the simula- tion approach with experimental measurements in explanted human atria and animal models. We will use the validated personalized modeling strategy to determine, in retrospective patient studies, what constitutes a set of optimal ablation lesions that terminate AF with the least likelihood of recurrence. The project will culminate with a pilot prospective patient study, where AF ablation will be executed directly at the simulation-predicted targets. Successful execution of the proposed studies will pave the way for a major paradigm shift in the clinical procedure of AF ablation in patients with fibrotic remodeling, resulting in a dramatic improvement in the efficacy of the therapy. Importantly, completion of this project will result in a major leap forward in the integration of computational modeling in the diagnosis and treatment of cardiac disease.

项目摘要 此应用程序是响应PAR-15-085：生物医学，生物， 行为，环境和临床研究（跨部门U 01），前沿挑战：预测 多尺度模型，以改善临床工作流程、标准操作程序、患者特异性 用于诊断和治疗计划的建模。 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。然而，手术的结果很差（50%的患者- 持续性房颤（PsAF）患者由于存在广泛的心房纤维化， 消融策略迫切需要新的方法，可以迅速和准确地识别， 确定PsAF的最佳消融靶点，从而提高疗效并增加耐受性 用于治疗，以及减少术后并发症和重复消融。 该应用程序的总体目标是开发和验证一种新的个性化多尺度 用于确定导管消融纤维化基质的最佳靶点的建模策略 PsAF患者我们建议开发和验证心房模型重建的磁共振图像的pa- 患有PsAF和纤维化重塑的患者。这些模型将从分子机制上整合功能， 水平的电生理相互作用在完整的器官。我们将参数化和验证模拟- 在移植的人类心房和动物模型中进行实验测量的方法。我们将使用 验证个性化建模策略，以确定在回顾性患者研究中， 最佳消融损伤，终止AF，复发可能性最小。该项目将以 一项先导性前瞻性患者研究，其中将直接在模拟预测的目标处执行AF消融。 拟议研究的成功执行将为临床研究的重大范式转变铺平道路。 在纤维化重构患者中进行AF消融术，导致 治疗的有效性。重要的是，该项目的完成将导致一个重大的飞跃， 心脏病诊断和治疗中计算建模的整合。