SBIR Phase I: Pre-Hospital Detection of Large Vessel Occlusion Strokes

这一小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是为急救人员配备一种客观工具，用于在送往医院的途中识别大血管闭塞(LVO)中风。这种对中风的快速而准确的分诊将使患者能够被送往最合适的护理环境，并减少干预时间。LVOS需要血管内治疗，只有综合性卒中中心才有能力进行这种治疗。如果一名左冠状动脉狭窄的患者仅仅因为距离较近而被送往没有血管内功能的医院，那么介入治疗的时间就会大大延长。在改善结果方面，实现最佳干预的时间是最重要的因素，在三个小时内取得最佳结果，在这一阈值下每个15分钟的窗口都有统计上的显著改善。中风是全球第二大死亡原因，也是导致长期残疾的主要原因，每年造成650亿美元的损失。在美国，每年有近80万人中风，其中40%的人永久残疾。该项目将简化院前环境中的中风分诊，以减少干预时间并改善中风患者的预后。这个小型企业创新研究(SBIR)第一阶段项目是一种基于EEG的产品，供EMS工作人员在院前环境中使用，以快速和客观地诊断疑似中风患者的LVO。在不到五分钟的时间内，EMS工作人员将能够部署、收集数据，并将分析结果显示在直观的仪表板上，以识别左冠状动脉狭窄的可能性，从而使EMS工作人员能够将患者路由到具有EVT功能的中风中心。当患者到达医院时，救护车内做出的决定将被传达给医生，医生随后可以立即开始干预，缩短从发病到干预的时间，并改善患者的短期和长期结果。使用大量硬件的中风患者的脑电数据的全面历史数据集将被用来开发一种机器学习模型，该模型可以将患者分类为LVO与非LVO中风以及中风与非中风。数据清理和特征提取的自动化将为我们的最终用户、急救医疗技术人员提供高度用户友好的体验和所需的工作流集成。最后，该模型将通过在两个临床站点收集的新型脑电数据进行验证，为监管互动奠定基础。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to arm emergency personnel with an objective tool for identifying large vessel occlusion (LVO) strokes while in route to the hospital. This rapid and accurate triage of stroke will enable routing of patients to the most appropriate care setting and reduce the time to intervention. LVOs require endovascular therapy which only comprehensive stroke centers have the capability to conduct. If a patient with an LVO is routed to a hospital without endovascular capabilities simply because it was closer, the time to intervention is extended drastically. When it comes to improving outcomes, time to optimal intervention is the most important factor with the best outcomes achieved under three hours and statistically significant improvements for each 15-minute window under that threshold. Stroke is the second leading cause of death and the primary cause of long-term disability worldwide costing the US $65B every year. Nearly 800,000 people suffer a stroke in the US annually and 40% are left with a permanent disability. The project will streamline stroke triage in the pre-hospital setting to reduce time to intervention and improve outcomes in stroke patients. This Small Business Innovation Research (SBIR) Phase I project an EEG-based product for EMS workers to use in the pre-hospital setting for the fast and objective diagnosis of LVO in suspected stroke patients. In under five minutes, EMS workers will be able to deploy, collect data, and have the analyzed results presented in an intuitive dashboard identifying the probability of an LVO, enabling EMS workers to route patients to stroke centers with EVT capabilities. When a patient arrives at the hospital, the determination made within the ambulance will be conveyed to physicians who can then immediately start intervention, reducing the time from onset to intervention and improving short and long-term patient outcomes. Comprehensive historical datasets of EEG-data from stroke patients using a broad array of hardware will be used to develop a machine learning model that can classify patients into LVO vs non-LVO stroke and stroke vs non-stroke. Automation of data cleaning and feature extraction will enable a highly user-friendly experience and the required workflow integration for our end-users, emergency medical technicians. Lastly, this model will be validated with novel EEG data collected at two clinical sites, laying the foundation for regulatory interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.