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.

项目摘要 心房颤动（AF）是一种影响520万美国人的高度流行的疾病，每例患者花费60 - 260亿美元。 一年，并增加心血管疾病，中风和死亡的风险。选择最佳治疗方法 每位房颤患者每天都面临临床挑战，因为没有一种治疗方法是所有病例中最好的。症状性 患者最常接受经皮或导管消融治疗，以隔离或破坏异常心房肌 组织.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素。 病理性心房纤维化是持续性房颤的主要原因， 发病机制，并提出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，以确定局部心房纤维化。心房纤维化导致电生理紊乱 并且还减弱局部心房力学、降低收缩性和增加僵硬度。对心房的影响 机械是很重要的。因此，我们假设，衰减的心房力学提供了一个强大的措施， 心房纤维化该项目的结果将是第一个组织学验证，可重现和可重复的 能够估计心房纤维化负荷的临床工具。 该基金的目的是利用心房纤维化和心房力学之间的机制联系， 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、组织学验证的心房纤维化的判别性指标。的 为实现这一目标，制定了以下目标。 AIM 1.稳健测量窦性心律和房颤时的3D心房CMR应变和刚度。 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协议使 测量心房力学，不需要对比或超声心动图的限制，也不需要辐射 的CT。我们试图通过识别受损的心房力学来检测心房纤维化。 AIM 2.对心房纤维化的CMR力学分类器进行建模和基准测试。最佳指数为 从心房力学中识别局部心房纤维化是未知的。训练和验证分类器需要一个 我们将直接使用组织学来测量基础事实。然后，将对分类器进行基准测试， 心房纤维化的常规标志物（电压标测和LGE-CMR）。 公共卫生意义-识别对特定治疗有反应的房颤（AF）患者 诸如消融的策略是心脏病专家每天面临的挑战。为每种AF选择最佳治疗 患者仍然是一个开放的挑战。这项工作的结果将使临床医生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房颤。

项目成果

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

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

Using synthetic data generation to train a cardiac motion tag tracking neural network.

• DOI: 10.1016/j.media.2021.102223
• 发表时间: 2021-12
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Loecher M;Perotti LE;Ennis DB
• 通讯作者: Ennis DB

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.

• DOI: 10.1371/journal.pcbi.1011257
• 发表时间: 2023-06
• 期刊: PLoS computational biology
• 影响因子: 4.3

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