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

SBIR 第一阶段：大血管闭塞中风的院前检测

• 批准号: 2213156
• 负责人: Ezekiel Fink
• 金额: $ 25.6万
• 依托单位: ASTERION AI INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213156&HistoricalAwards=false
• 关键词: SBIR; Phase; Pre; Hospital; Detection

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.

该小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是为急救人员提供一种客观工具，用于在前往医院的途中识别大血管闭塞（LVO）卒中。这种快速准确的卒中分类将使患者能够选择最合适的护理环境，并减少干预时间。LVO需要血管内治疗，只有综合性卒中中心才有能力进行。如果LVO患者被送往没有血管内治疗能力的医院，仅仅是因为距离较近，则介入时间会大幅延长。当涉及到改善结果时，达到最佳干预的时间是最重要的因素，在3小时内实现最佳结果，并且在该阈值下每15分钟窗口的统计学显着改善。中风是全球第二大死亡原因和长期残疾的主要原因，每年花费650亿美元。美国每年有近80万人中风，40%的人留下永久性残疾。该项目将简化院前环境中的卒中分诊，以减少干预时间并改善卒中患者的结局。这项小型企业创新研究（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，用于快速客观地诊断疑似卒中患者的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中，以识别LVO的概率，使EMS工作人员能够将患者送往具有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传达给医生，然后医生可以立即开始干预，减少从发病到干预的时间，并改善患者的短期和长期结果。使用广泛的硬件阵列从卒中患者获得的EEG数据的综合历史数据集将用于开发机器学习模型，该模型可以将患者分类为LVO与非LVO卒中以及卒中与非卒中。数据清理和特征提取的自动化将为我们的最终用户，紧急医疗技术人员提供高度用户友好的体验和所需的工作流程集成。最后，该模型将通过在两个临床站点收集的新EEG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，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。