The NIH SPAN Coordinating Center

我们建议继续一个新的，自适应的，安全的系统，并行测试有前途的干预措施 与单独再灌注相比，旨在改善缺血性卒中后的结局， 评估网络。SPAN将筛选和选择非常有希望的候选干预措施， 在StrokeNet中。申请人PI和研究团队-SPAN协调中心（CC）-创建并 实施了新的解决方案，以解决阻碍成功实施临床前网络的几个关键障碍。 其结果是一个非常成功的，多站点的网络，用于测试动物中风中的假定脑保护剂 模型包括相同数量的女性和男性以及合并症，如衰老、高血压和 高血糖症我们发明了新的病例登记和跟踪方法：集中随机化; 集中药物制备/掩蔽/装瓶;对外科医生进行分组隐藏;集中、设盲 行为评估;以及MRI扫描的集中盲法评估。SPAN的各个方面都经历过 一个彻底的，有组织的过程：文献审查，多轮辩论和重新审查，最后决定是 制作并记录。利用这一过程，SPAN制定了关于动物模型选择的SOP， 手术方法、行为评估、评估员培训和认证。SPAN使用多臂多臂 阶段设计和最先进的实验严谨性，以成功地减少或消除常见的 bias.中风管理和干预措施是以盲态方式提供的。结果数据（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分员。我们，SPAN CC，协调，网络 通过与研究中心的日常联系进行沟通;每周入组更新报告;每月指导委员会 会议;年度研究者会议;和半年一次的研究中心访视。为了提高所有实验室的重现性， 我们为所有外科医生和行为评分员设计了培训课程和认证测试， 任务，例如，手术，记录行为测试，角落测试评级等。这些培训和认证工具将 便于在SPAN 2.0中快速、严格地添加新的测试实验室。事实上，在整个SPAN 1.0中，所有SOP， 协议和基础设施的设计，以促进快速，简单地添加新的网站和容易的过渡 以最短的停机时间升级到SPAN 2.0。目前，我们正在为下一次融资设计创新的增强措施， 循环：我们正在训练一种机器学习算法来对行为视频进行评分， 使用数字管道进行评分;我们正在开发一种血凝块/血栓溶解模型， 存在血栓溶解剂。目前的应用程序，如果得到资助，将允许SPAN CC继续改进 通过实施关键技术创新，包括新的 使用机器学习的评估工具。SPAN 2.0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一个模式。

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.