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: 10670803
• 负责人: Daniel B Ennis
• 金额: $ 67.13万
• 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/10670803
• 关键词: 3-Dimensional; 3D Print; Ablation; Acceleration; 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; Precision therapeutics; Prediction of Response to Therapy; Process; Protocols documentation; Public Health; Radiation; Recurrence; Reporting; Reproducibility; Risk; Sinus; Stroke; Techniques; Tissues; Training; Transplant Recipients; Transplantation; Validation; Ventricular; Work; atrioventricular node; cardiac magnetic resonance imaging; cardiovascular disorder risk; cost; design; disease phenotype; heart rhythm; image processing; implantation; in vivo; indexing; mathematical model; open source; optimal treatments; personalized medicine; pharmacologic; 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.

项目概要 心房颤动 (AF) 是一种非常流行的疾病，影响着 520 万美国人，每年造成 6-260 亿美元的损失 年，并增加心血管疾病、中风和死亡的风险。 每个 AF 患者仍然面临着日常的临床挑战，因为没有一种治疗方法对于所有症状都是最好的。 患者最常接受药物治疗，或通过导管消融来隔离或破坏异常心房 然而，这两种方法通常都是无效的，并且没有一致的反应预测因子。 病理性心房纤维化是持续性房颤的主要原因，已多次被认为与其相关 发病机制并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)。 基于力学的方法来识别局部心房纤维化会导致电生理学混乱。 并且还会减弱局部心房力学，降低收缩力，增加对心房的影响。 因此，我们必须通过衰减的心房力学来提供强有力的测量。 该项目的结果将是第一个经过组织学验证、可重现和可重复的结果。 能够估计心房纤维化负担的临床工具。 这笔赠款的目的将利用心房纤维化和心房力学之间的机制联系来开发 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。 建立一种基于力学的、有区别的、经组织学验证的心房纤维化测量方法。 为了实现这一目标，我们制定了以下目标。 目标 1. 稳健地测量窦性心律和 AF 中的 3D 心房 CMR 应变和硬度 - 甚至心房运动。 AF 期间 – 在 CMR 上很明显。我们的自由呼吸和心律失常不敏感 CMR 协议可实现。 测量心房力学，无需造影剂或超声心动图的限制，也无需辐射 我们试图通过识别受损的心房力学来检测心房纤维化。 目标 2. 验证基于 CMR 力学的心房纤维化分类器并对其进行基准测试最佳指数。 从心房力学中识别局部心房纤维化尚不清楚，训练和验证分类器需要 基本事实，我们将使用组织学直接测量，然后将分类器作为基准。 心房纤维化的常规标志物（电压测绘和 LGE-CMR）。 公共卫生意义 – 识别对特定治疗有反应的房颤 (AF) 患者 消融等策略是心脏病专家每天面临的挑战。为每种房颤选择最佳治疗方法。 患者仍然是一个公开的挑战，这项工作的结果将使上级能够更好地管理患者。 通过使用基于心脏 MRI 的方法帮助识别心房纤维化负担来治疗心房颤动。

项目成果

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate.

• DOI: 10.7554/elife.64213
• 发表时间: 2021-05-04
• 期刊: eLife
• 影响因子: 7.7
• 作者: Bifulco SF;Scott GD;Sarairah S;Birjandian Z;Roney CH;Niederer SA;Mahnkopf C;Kuhnlein P;Mitlacher M;Tirschwell D;Longstreth WT;Akoum N;Boyle PM
• 通讯作者: Boyle PM

Cost-effectiveness analysis of leadless cardiac resynchronization therapy.

无引线心脏再同步治疗的成本效益分析。

• DOI: 10.1111/jce.16102
• 发表时间: 2023
• 期刊: Journal of cardiovascular electrophysiology
• 影响因子: 2.7
• 作者: Wijesuriya,Nadeev;Mehta,Vishal;Vere,FelicityDe;Howell,Sandra;Behar,JonathanM;Shute,Andrew;Lee,Michael;Bosco,Paolo;Niederer,StevenA;Rinaldi,ChristopherA
• 通讯作者: Rinaldi,ChristopherA

Myocardial Segmentation of Tagged Magnetic Resonance Images with Transfer Learning Using Generative Cine-To-Tagged Dataset Transformation.

• DOI: 10.3390/bioengineering10020166
• 发表时间: 2023-01-28
• 期刊: Bioengineering (Basel, Switzerland)

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.

• DOI: 10.3389/fphys.2021.646023
• 发表时间: 2021
• 期刊: Frontiers in physiology
• 影响因子: 4
• 作者: Williams SE;Roney CH;Connolly A;Sim I;Whitaker J;O'Hare D;Kotadia I;O'Neill L;Corrado C;Bishop M;Niederer SA;Wright M;O'Neill M;Linton NWF
• 通讯作者: Linton NWF

Editorial: Future directions in conduction system pacing to achieve cardiac resynchronization.

• DOI: 10.3389/fphys.2023.1281552
• 发表时间: 2023
• 期刊: Frontiers in physiology
• 影响因子: 4