European training network for developing smart neuromonitoring solutions to support precision medicine in acute central nervous injury

欧洲培训网络开发智能神经监测解决方案以支持急性中枢神经损伤的精准医疗

• 批准号: EP/Y03256X/1
• 负责人: Peter Smielewski
• 金额: $ 33.22万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY03256X%2F1
• 关键词: European; training; network; developing; smart

In traumatic brain/spinal cord injury and stroke - major causes of death and disability -, progress has come from monitored intensive care and swift action upon detection of secondary insults. Although it is possible to monitor intracranial pressure, the brain & spinal cord still behave as a black box. Current measurable signals only roughly represent ongoing pathophysiological processes, and dynamic insults (eg impaired autoregulation and neurovascular unit dysfunction) cannot be reliably monitored. As a result, no therapeutic action has been shown to be beneficial in randomised patient trials. The project goal is to prepare novel dynamic insult monitoring technologies and to develop improved decision support by integrating disease models and insult/treatment ontologies into smart multimodality monitor software. A parallel goal is to unite high level expertise in clinical, biomedical, statistical and engineering sciences into one network to boost the next generation of researchers to substantially advance the field of neuromonitoring. The network includes 3 relevant animal models and access to large (multi)center patient databases with injury, treatment & outcome data (eg Center-TBI).Direct autoregulation visualization in the cranial window piglet model will be elaborated to improve circulation models and relationswith measurable high resolution signals to develop a real-time autoregulation monitor. These metrics will be associated with spreading depolarizations, vasospasm, ischemia and brain function in the rodent stroke models. The models and monitor technology are highly transferrable to patient care. Patient data will be used to build multidimensional statistical disease models. Insult and treatment ontologies will be developed in parallel with insult prediction and insult burden visualization concepts. Smart monitor platforms that aid precision medicine in acute central nervous system injury close to trials and future innovation leaders are expected results.

在创伤性的大脑/脊髓损伤和中风中 - 死亡和残疾的主要原因 - 进展来自受监测的重症监护和迅速的行动，发现次要侮辱。尽管可以监测颅内压，但大脑和脊髓仍然表现为黑匣子。当前可测量的信号仅大致表示正在进行的病理生理过程，并且无法可靠地监测动态损伤（例如自动调节受损和神经血管单位功能障碍）。结果，没有证明对随机患者试验有益的治疗作用。项目目标是准备新型的动态侮辱监控技术，并通过将疾病模型和侮辱/治疗本体结合到智能多模式监视器软件中，以改进决策支持。一个平行的目标是将临床，生物医学，统计和工程科学领域的高水平专业知识统一到一个网络中，以增强下一代研究人员，从而大大推动神经监测的领域。该网络包括3个相关的动物模型，并访问具有伤害，治疗和结果数据（例如中心-TBI）的大型（多）中心患者数据库。将详细阐述颅窗模型中的直接自动调节可视化，以改善循环模型和关系可测量的高分辨率信号，以开发实时自动调查监控器。这些指标将与啮齿动物中风模型中的扩散去极化，血管痉挛，缺血和脑功能有关。模型和监测技术高度转移到患者护理中。患者数据将用于构建多维统计疾病模型。侮辱和治疗本体将与侮辱性预测和侮辱性的可视化概念并行开发。预期的智能监测平台可在急性中枢神经系统伤害和未来创新领导者接近的急性中枢神经系统伤害中得到预期。