课题基金 / 基金详情

ERIS - Effective Reserve In Stroke

ERIS - Effective Reserve In Stroke
ERIS - 有效储备冲程
批准号:
10724761
负责人:
Markus D Schirmer
金额:
$45.42万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-01 至 2025-08-31

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中文摘要
翻译
项目总结/摘要 中风和痴呆是导致年龄相关疾病负担和长期残疾的两个主要因素。 随着美国和全球人口老龄化,中风的患病率增加, 预防脑血管疾病和病理引起的相关长期功能障碍。了解 可能导致功能独立的中风后结局决定因素具有重要的临床意义 并且对于开发针对性治疗方案至关重要。在神经退行性疾病领域, 结构性和功能性储备被用来解释比预期更好的认知结果。 储备描述了大脑对病理的补偿能力。特别是在中风中, 保护机制对于理解结果具有重要的实际意义,然而,很少有研究 在中风人群中研究这些概念。结构性储备一般旨在量化最大 一个人的大脑储备,而预先存在的疾病负担没有考虑在内。其余,有效 然而,储备更可能反映了大脑对突发血管事件的补偿能力。整体 本项目的目标是建立一个翻译的方法来估计有效储量从低分辨率 急性缺血性中风患者在急诊室获得的临床成像数据, 并通过合并纵向和空间病变信息来扩展概念,以帮助改善结果 建模目的1探讨有效储备的概念及其在急性缺血性脑卒中患者中的可行性 中风我们利用两个大规模、回顾性中风队列,并提供临床成像(单个中心: N=453,多部位:N=912)。为确保有效储备的转化性质,我们将加强现有和 开发新的支持深度学习的图像处理管道,以提取所需的定量成像 生物标志物。这些生物标志物将用于模拟功能结果,以改良的兰金测量 量表评分,卒中后约90天。目标2将制定纵向评估方法, 储备和研究卒中后有效储备的下降如何影响卒中结局。目标3将进一步 通过利用中风病变的空间信息并确定其对脑卒中的影响, 大脑对病理的补偿能力该项目的一个重要和独特的特点在于其直接 翻译潜力,因为它是在急诊室获得的中风神经成像数据评估, 回答关于大脑补偿急性血管事件的能力及其重点的重要问题, 大脑中与大脑健康有关的保护机制。为了实现这些目标,该项目利用了独特的 数据和多学科专业知识,以创建临床可用的纵向和空间特异性生物标志物 这增强了实时中风诊断,并可以指导个性化的患者护理,以改善 结果。
英文摘要
PROJECT SUMMARY/ABSTRACT Stroke and dementia are two of the leading contributors to age-related disease burden and long-term disability. With aging populations in the US and worldwide, the prevalence of stroke increases and it becomes imperative to prevent related long-term functional disability from cerebrovascular disease and pathology. Understanding the determinants of post-stroke outcome that may lead to functional independence is of great clinical significance and essential to developing targeted treatment options. In the field of neurodegenerative diseases, the concepts of structural and functional reserve have been invoked to explain better than expected cognitive outcomes. Reserve describes the brain’s capacity to compensate for pathology. Specifically in stroke, elucidating such protective mechanisms has major practical implications for understanding outcome, however, few studies have investigated these concepts in stroke populations. Structural reserve generally aims to quantify the maximum brain reserve of a person, while pre-existing disease burden is not accounted for. The remaining, effective reserve, however, more likely reflects the brain’s ability to compensate for a sudden vascular event. The overall goal of this project is to create a translational approach to estimate the effective reserve from the low-resolution clinical imaging data of patients with acute ischemic stroke as they are acquired in the emergency department, and expand the concept by incorporating longitudinal and spatial lesion information to help improve outcome modeling. Aim 1 will investigate the concept of effective reserve and its feasibility in patients with acute ischemic stroke. We leverage two large-scale, retrospective stroke cohorts with clinical imaging available (single site: N=453, multi-site: N=912). To ensure the translational nature of effective reserve, we will enhance existing and develop new deep-learning enabled image processing pipelines to extract the required quantitative imaging biomarkers. These biomarkers will then be used to model functional outcome, measured as modified Rankin Scale score, ~90 days post-stroke. Aim 2 will develop longitudinal assessment methodologies for effective reserve and investigate how a drop in effective reserve post-stroke impacts stroke outcome. Aim 3 will further enhance this principle by harnessing spatial information of the stroke lesion and determining its impact on the brain’s ability to compensate for pathology. An important and unique feature of this project lies in its direct translational potential, as it is assessed on stroke neuroimaging data acquired in the emergency department to answer important questions about the brain’s ability to compensate for the acute vascular event and its focus on protective mechanisms in the brain relating to brain health. To achieve these goals, this project leverages unique data and multidisciplinary expertise, to create a clinically available, longitudinal, and spatially specific biomarker that enhances real-time stroke prognostication, and which can guide individualized patient care to improve outcomes.
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