Using Atrial Mechanics To Identify Fibrosis In Patients with Atrial Fibrillation

利用心房力学识别心房颤动患者的纤维化

• 批准号: 10201745
• 负责人: Daniel B Ennis
• 金额: $ 70.18万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-24 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201745
• 关键词: 3-Dimensional; 3D Print; Ablation; Adoption; Affect; Algorithms; American; Arrhythmia; Atrial Fibrillation; Attenuated; Benchmarking; Biological Markers; Breathing; Cardiac; Cardiac ablation; Cessation of life; Cicatrix; Clinical; Complex; Coupled; Dependence; Disease; Echocardiography; Electric Countershock; Electrophysiology (science); Fibrosis; Fostering; Gadolinium; Grant; Guidelines; Heart Atrium; Heart-Lung Transplantation; Histologic; Histology; Image; Impairment; Infiltration; Laws; Link; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mechanics; Methods; Motion; Myocardial; Pacemakers; Pathogenesis; Pathologic; Patients; Periodicity; Pharmacological Treatment; Pharmacology; Precision therapeutics; Process; Protocols documentation; Public Health; Radiation; Reporting; Reproducibility; Risk; Sinus; Stroke; Techniques; Tissues; Training; Transplant Recipients; Transplantation; Ventricular; Work; atrioventricular node; base; cardiovascular disorder risk; cost; design; heart rhythm; image processing; imaging modality; implantation; in vivo; indexing; mathematical model; open source; optimal treatments; personalized medicine; predicting response; scaffold; side effect; success; tool; treatment strategy; voltage

PROJECT SUMMARY Atrial fibrillation (AF) is a highly prevalent disease affecting 5.2 million Americans, costs the US $6-26 billion per year, and increases the risk of cardiovascular disease, stroke, and death. Selecting the optimal treatment for each AF patient remains a daily clinical challenge as no single treatment is best in all cases. Symptomatic patients are most frequently treated pharmacologically, or by catheter ablation to isolate or destroy aberrant atrial tissue. However, both are commonly ineffective and there are no consistent predictors of response. Pathological atrial fibrosis is a major contributor to sustaining AF, has repeatedly been implicated in its pathogenesis and is proposed as a biomarker for personalizing treatment. We propose to use cardiac MRI (CMR) mechanics-based measures to identify localized atrial fibrosis. Atrial fibrosis fosters chaotic electrophysiology and also attenuates local atrial mechanics, decreases contractility, and increases stiffness. The impact on atrial mechanics is substantial. Therefore, we hypothesize that attenuated atrial mechanics provide a robust measure of atrial fibrosis. The result of this project will be the first histologically validated, reproducible and repeatable clinical tool that enables estimation of atrial fibrosis burden. The aims of this grant will exploit the mechanistic link between atrial fibrosis and atrial mechanics to develop and validate a clinical workflow for measuring a mechanics-based classifier of fibrosis. The overall objective is to establish a mechanics-based and discriminatory measure of histologically validated atrial fibrosis. The following aims are designed to achieve this objective. AIM 1. To robustly measure 3D atrial CMR strain and stiffness in sinus rhythm and AF. Atrial motion – even during AF – is readily apparent on CMR. Our free-breathing and arrhythmia insensitive CMR protocol enables measuring atrial mechanics without the need for contrast or the limitations of echocardiography, nor the radiation of CT. We seek to detect atrial fibrosis by identifying impaired atrial mechanics. AIM 2. Validate and benchmark a CMR mechanics-based classifier of atrial fibrosis. The optimal index for identifying local atrial fibrosis from atrial mechanics is not known. Training and validating a classifier requires a ground truth, which we will measure directly using histology. The classifier will then be benchmarked against conventional markers of atrial fibrosis (voltage mapping and LGE-CMR). Public Health Significance – Identifying patients with atrial fibrillation (AF) that will respond to specific treatment strategies such as ablation is a daily challenge for cardiologists. Selecting the optimal treatment for each AF patient remains an open challenge. The results of this work will enable clinicians to better manage patients with atrial fibrillation by helping to identify the atrial fibrosis burden using cardiac MRI based methods.

项目摘要 心房颤动（AF）是一种高度普遍的疾病，影响了520万美国人，耗资6-26亿美元 一年，并增加了心血管疾病，中风和死亡的风险。选择最佳治疗 每个AF患者仍然是每天的临床挑战，因为在所有情况下，没有任何单一治疗是最好的。有症状 患者在药理学上最常治疗，或者通过导管消融以隔离或破坏异常心房 组织。但是，两者通常都是无效的，并且没有响应的一致预测指标。 病理心房纤维化是维持AF的主要因素，反复牵涉到其 发病机理，被提议作为个性化治疗的生物标志物。我们建议使用心脏MRI（CMR） 基于力学的措施，以鉴定局部的心房纤维化。心房纤维化促进混沌电生理学 还会减轻局部心房力学，降低收缩力并增加刚度。对心房的影响 力学很重要。因此，我们假设减弱的心房力学提供了强大的测量 心房纤维化。该项目的结果将是第一个经过组织学验证，可再现且可重复的结果 临床工具，可以估计心房纤维化负担。 这笔赠款的目的将利用心房纤维化与心房力学之间的机械联系来发展 并验证用于测量基于力学的纤维化分类器的临床工作流程。总体目标是 建立基于力学的基于组织学验证的心房纤维化的歧视性测量。 以下目标旨在实现这一目标。 目的1。牢固地测量3D心房CMR应变和鼻窦节律和AF中的刚度。心房运动 - 甚至 在AF期间 - 在CMR上很明显。我们的自由呼吸和心律不齐的CMR协议可以启用 测量心房力学无需对比度或超声心动图的局限性，也没有辐射 Ct。我们试图通过鉴定心房力学受损来检测房屋纤维化。 AIM 2。验证和基准测试基于CMR的心房纤维化分类器。最佳索引 尚不清楚从心房力学中识别局部房屋纤维化。培训和验证分类器需要一个 地面真理，我们将使用组织学直接衡量。然后，分类器将以基准为基准 心房纤维化的常规标记（电压映射和LGE-CMR）。 公共卫生意义 - 识别患有房颤的患者（AF），这些患者将对特定治疗做出反应 诸如消融之类的策略是心脏病专家的日常挑战。为每个AF选择最佳治疗 病人仍然是一个公开的挑战。这项工作的结果将使临床医生能够更好地管理 通过使用基于心脏MRI的方法来帮助鉴定心房纤维化伯恩的心房颤动。