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.

在创伤性脑/脊髓损伤和中风（死亡和残疾的主要原因）方面，进展来自于监测重症监护和在发现继发性损伤后迅速采取行动。虽然可以监测颅内压，但大脑和脊髓仍然像一个黑匣子。目前可测量的信号只能粗略地代表正在进行的病理生理过程，动态损伤（如自我调节受损和神经血管单元功能障碍）不能可靠地监测。因此，在随机患者试验中，没有任何治疗作用被证明是有益的。该项目的目标是准备新的动态侮辱监测技术，并通过将疾病模型和侮辱/治疗本体集成到智能多模态监测软件中来开发改进的决策支持。另一个并行的目标是将临床、生物医学、统计和工程科学的高水平专业知识整合到一个网络中，以促进下一代研究人员实质性地推进神经监测领域。该网络包括3个相关的动物模型，并可访问大型（多）中心患者数据库，其中包含损伤、治疗和结果数据（例如center - tbi）。在颅窗仔猪模型中的直接自动调节可视化将被详细阐述，以改进循环模型和与可测量的高分辨率信号的关系，以开发实时自动调节监视器。这些指标将与鼠类中风模型中的扩张性去极化、血管痉挛、缺血和脑功能相关。这些模型和监测技术可高度转移到患者护理中。患者数据将用于建立多维统计疾病模型。侮辱和治疗本体将与侮辱预测和侮辱负担可视化概念并行发展。帮助精准医学治疗急性中枢神经系统损伤的智能监测平台接近试验和未来创新领导者的预期结果。