The NIH SPAN Coordinating Center

NIH SPAN 协调中心

• 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
• 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

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.

我们建议继续一个新颖，自适应，有安全的系统，以并行测试承诺干预措施 与仅再灌注相比，旨在改善缺血性中风后的结果 评估网络。跨度将筛选并选择高度希望的候选干预措施以进行研究 在Strokenet。申请人PI和研究团队（跨度协调中心（CC））创建并 实施了新的解决方案，以阻碍成功临床前网络实施的几个关键障碍。 结果是一个非常成功的多站点网络，用于测试动物中风中推定的脑保护剂 包括相等数量的女性和男性以及合并症的模型，例如衰老，高血压和 高血糖。我们发明了新颖的病例入学和跟踪方法；集中式随机化 ; 集中式药物准备/掩盖/装瓶；对外科医生的群体隐瞒；集中，盲目 行为评估；以及对MRI扫描的集中式评估。跨度的每个方面都通过 一个彻底的，有条理的过程：文学审查，辩论和重新审查的回合，最后是决定 制作和记录。使用此过程，SPAN开发了有关动物模型选择的SOP， 手术方法，行为评估，评估人员培训和认证。跨度使用多臂多臂 舞台设计和最先进的实验性严格性，以成功减少或消除常见来源 偏见。进行中风，并以盲目的方式提供干预措施。结果数据（行为测试 和MRI扫描）由跨度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救援人员。我们，跨度CC，坐标，网络 通过与网站的日常接触进行通信；每周入学更新报告；每月指导委员会 会议；年度调查员会议；和半年度现场访问。为了提高所有实验室的可重复性， 我们为所有针对特定的外科医生和行为评估者设计了培训课程和认证测试 任务，例如手术，记录行为测试，角落测试等级等。这些培训和认证工具将 在SPAN 2.0中促进快速，严格添加新的测试实验室。实际上，整个跨度1.0，所有sops， 协议和基础架构旨在促进新站点的快速，简单的添加和易于过渡 以最小的停机时间跨度2.0。目前，我们正在为下一个资金设计创新的增强功能 周期：我们正在训练机器学习算法以评分行为视频，以允许快速，可重复 使用数字管道得分；我们正在开发血凝块/溶栓模型，以允许在 目前的申请（如果资助）将允许SPAN CC继续改进 并通过实施关键的技术创新来推进临床前开发，包括新颖 使用机器学习的评估工具。 Span 2.0将继续我们成功入学的记录 和技术创新，为其他疾病领域的临床前网络提供了模型。