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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结