The NIH SPAN Coordinating Center

NIH SPAN 协调中心

基本信息

批准号：
10591751
负责人：
Patrick D Lyden
金额：
$ 100.85万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10591751
关键词：
Address Aging Animal Model Animals Area Artificial Intelligence Assessment tool Award Behavior Behavior assessment Behavioral Blinded Blood coagulation Certification Clinical Research Clinical Trials Communication Data Databases Disease Drug Compounding Elements Enrollment Evaluation Experimental Designs Female Funding Futility Future Goals Histologic Hyperglycemia Hypertension Image Individual Infrastructure Intervention Ischemic Stroke Laboratories Lead Lesion MRI Scans Machine Learning Magnetic Resonance Imaging Methods Mind Modeling Modernization Monitor Mus National Institute of Neurological Disorders and Stroke Operative Surgical Procedures Outcome Outcome Assessment Phase Placebo Control Pre-Clinical Model Preclinical Testing Procedures Process Production Protocols documentation Randomized Rattus Reperfusion Therapy Reporting Reproducibility Research Research Personnel Review Literature Secure Site Site Visit Source Specific qualifier value Stroke Support System Surgeon System Testing Therapeutic Time Training Uncertainty United States National Institutes of Health Update arm behavior test behavioral outcome blind clinically relevant comorbidity design digital experience falls improved improved outcome innovation invention machine learning algorithm male meetings morphometry novel phase 2 testing power analysis pre-clinical pre-clinical assessment preclinical development primary outcome repository secondary outcome stroke model stroke therapy success therapy design thrombolysis tool

项目摘要

We propose to continue a novel, adaptive, secured system for parallel testing of promising interventions designed to improve outcome after ischemic stroke when compared to reperfusion alone, the Stroke Preclinical Assessment Network. SPAN will screen and select highly promising candidate interventions for possible study in StrokeNet. The applicant PI and research team—the SPAN Coordinating Center (CC)—created and implemented novel solutions to several key barriers impeding successful pre-clinical network implementation. The result is a highly successful, multi-site network for testing putative cerebroprotectants in animal stroke models that include equal numbers of females and males and co-morbidities such as aging, hypertension and hyperglycemia. We invented novel methods of case enrollment and tracking; centralized randomization; centralized drug preparation/masking/bottling; group concealment from the surgeons; centralized, blinded behavioral assessment; and centralized, blinded evaluation of MRI scans. Each aspect of SPAN went through a thorough, organized process: literature review, rounds of debate and re-review, and finally decisions were made and documented. Using this process, SPAN developed SOPs concerning the choice of animal models, surgical methods, behavior assessments, assessor training and certification. SPAN uses a Multi-Arm Multi- Stage design and state-of-the-art experimental rigor to successfully reduce or eliminate common sources of bias. Stroke is administered and interventions are provided in blind fashion. Outcome data (behavioral tests and MRI scans) are uploaded by the SPAN Testing Laboratories to a central repository, and then randomly assigned by the CC to independent raters at other labs. We, the SPAN CC, coordinate, network communication via daily contact with the sites; weekly enrollment update reports; monthly Steering Committee meetings; annual investigator meetings; and semi-annual site visits. To improve reproducibility across all labs, we devised training sessions and certification tests for all surgeons and behavioral raters targeted to specific tasks, e.g., surgery, recording behavior testing, corner test rating, etc. These training and certification tools will facilitate rapid, rigorous addition of new Testing Labs in SPAN 2.0. Throughout SPAN 1.0 in fact, all SOPs, protocols, and infrastructure were designed to facilitate rapid, simple addition of new sites and easy transition to SPAN 2.0 with minimal down-time. Presently, we are devising innovative enhancements for the next funding cycle: we are training a machine learning algorithm to score behavior videos to allow rapid, reproducible scoring using a digital pipeline; we are developing a blood clot/thrombolysis model to allow testing in the presence of thrombolytics. This present application, if funded, will allow the SPAN CC to continue to improve and advance preclinical development by implementing critical technical innovations, including novel assessment tools using machine learning. SPAN 2.0 will continue our track record of successful enrollment and technical innovation, providing a model for pre-clinical networks in other disease areas.

我们建议继续采用一种新颖的、自适应的、安全的系统来并行测试有希望的干预措施与单独再灌注相比，旨在改善缺血性中风后的结果，中风临床前研究评估网络将筛选并选择极具前景的候选干预措施以进行可能的研究。申请人的 PI 和研究团队——SPAN 协调中心 (CC)——创建并针对阻碍临床前网络成功实施的几个关键障碍实施了新颖的解决方案。结果是一个非常成功的多站点网络，用于测试动物中风的假定脑保护剂模型包括相同数量的女性和男性以及衰老、高血压和我们发明了病例登记和跟踪的新方法；集中药物制备/掩蔽/装瓶；对外科医生进行集中、盲法；行为评估；以及 MRI 扫描的集中、盲法评估。一个彻底、有组织的过程：文献综述、多轮辩论和重新审查，最后做出决定利用这一过程，SPAN 制定了有关动物模型选择的 SOP，手术方法、行为评估、评估员培训和认证使用 Multi-Arm Multi-。阶段设计和最先进的实验严谨性可成功减少或消除常见的来源中风的管理和干预措施以盲法方式提供（行为测试）。和 MRI 扫描）由 SPAN 测试实验室上传到中央存储库，然后随机由 CC 分配给其他实验室的独立评估者我们，SPAN CC，协调网络。通过与站点的日常联系进行沟通；每周的招生更新报告；会议；年度研究者会议；以及半年一次的现场访问，以提高所有实验室的可重复性。我们为所有外科医生和行为评估者设计了针对特定领域的培训课程和认证测试任务，例如手术、记录行为测试、角落测试评级等。这些培训和认证工具将事实上，在整个 SPAN 1.0 中，所有 SOP 都有助于在 SPAN 2.0 中快速、严格地添加新的测试实验室。协议和基础设施的设计旨在促进快速、简单地添加新站点并轻松过渡以最短的停机时间实现 SPAN 2.0 目前，我们正在为下一次融资设计创新的增强功能。周期：我们正在训练一种机器学习算法来对行为视频进行评分，以实现快速、可重复的结果使用数字管道进行评分；我们正在开发血凝块/溶栓模型，以便在如果获得资助，本申请将允许 SPAN CC 继续改进。通过实施关键技术创新（包括新颖的技术）来推进临床前开发使用机器学习的评估工具将延续我们成功注册的记录。和技术创新，为其他疾病领域的临床前网络提供了模型。