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

在创伤性脑/脊髓损伤和中风-死亡和残疾的主要原因-方面，进展来自监测重症监护和在发现二次损伤时迅速采取行动。虽然可以监测颅内压，但大脑和脊髓仍然表现为黑匣子。当前可测量的信号仅粗略地表示正在进行的病理生理过程，并且不能可靠地监测动态损伤（例如，受损的自动调节和神经血管单元功能障碍）。因此，在随机患者试验中，没有治疗作用显示出有益。该项目的目标是准备新的动态损伤监测技术，并通过将疾病模型和损伤/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。一个平行的目标是将临床，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大幅推进神经监测领域。该网络包括3个相关的动物模型和访问大型（多）中心患者数据库的损伤，治疗和结果数据（如中心TBI）。将详细说明在颅窗仔猪模型中的直接自动调节可视化，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测器。这些指标将与啮齿动物卒中模型中的扩散性去极化、血管痉挛、缺血和脑功能相关。模型和监测技术可高度转移到患者护理中。患者数据将用于构建多维统计疾病模型。侮辱和治疗本体论将与侮辱预测和侮辱负担可视化概念并行开发。智能监测平台，帮助精密医疗在急性中枢神经系统损伤接近试验和未来的创新领导者预期的结果。

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.