Bio-digital Rapid Alert to Identify Neuromorbidity

无声的发展和进展的神经疾病，或神经疾病，在住院， 危重病人代表了一种新认识和新出现的流行病。这包括入院的患者 有初级神经学诊断的重症监护病房，那些基于潜在的风险增加的 疾病(例如，包括COVID19在内的嗜神经性病毒感染)，以及 神经疾病是难以捉摸和意想不到的。与危重疾病相关的神经病变可由以下原因引起 生理不稳定，生化紊乱，药物副作用，侵入性机械支持， 不动，和/或用于防止死亡的其他疗法。它的年龄跨度从新生儿到 老年人，发生在性别和种族之间，在患有全身疾病的患者中被低估(例如，败血症， 病毒感染和其他炎症情况)和严重器官衰竭(例如急性呼吸窘迫 综合症、癌症、肝肾功能衰竭)。在美国，神经发病率从5%到47%不等。 重病儿童和成人，因此每年影响数十万名患者。经常 神经病变在临床表现出现之前会在未被发现的情况下发展，并且是不可逆转的。神经病变 可剧烈发作，例如癫痫发作、中风、脑出血、脑水肿和/或精神错乱，或在 更持久的时尚，例如神经肌肉无力和/或认知能力下降，通常是永久性的， 在人的余生中一直忍受着。目前还没有标准的临床工具来识别患者 神经疾病的风险或实时神经监测，与心脏、肾脏、肝脏和 许多其他器官。 为了填补这一空白并改变临床医生检测和监测演变中的脑损伤的方式，我们开发了 一种生物数字快速警报，用于识别持续提供电子健康记录的神经疾病(脑) 将9个临床领域(A至I)的(EHR)变量整合到专有信息学和机器学习平台中。 原型脑A-I模型是稳健的，并在临床行动之前预测临床医生对神经疾病的担忧 有人了。为了将生物和数字签名联系起来，我们定义了一组血清生物标记物，可以识别 在临床检测之前有时间记录的神经发病率。使用“贝叶斯到床边”的方法，我们有 创建了连接EHR和专用主机服务器的实时数据管道，建立了基础设施 将Brain A-I作为嵌入式预测分析和决策驱动支持工具运行所必需的。 在这个提案中，我们将测试这样的假设，即电子病历中的数字签名与特定于大脑的 生物标记物可以快速检测危重儿童的神经发病率。成功部署可互操作、 24/7全天候护理点神经学监测用于早期发现神经发病率将是一项突破 用于危重病人的临床管理。

The silent development and progression of neurologic morbidity, or neuromorbidity, among hospitalized, critically ill patients represents a newly recognized and emerging epidemic. This includes patients admitted to intensive care units with primary neurologic diagnoses, those at increased risk based on their underlying disease (e.g. neurotropic viral infections including COVID19), and those where the development of neuromorbidity is occult and unexpected. Neuromorbidity associated with critical illness can be caused by physiologic instability, biochemical derangements, side effects of medications, invasive mechanical support, immobility, and/or other therapies used to prevent death. It spans the age spectrum from neonates to the elderly, occurs across gender and race, and is underrecognized in patients with systemic illnesses (e.g. sepsis, viral infections, and other inflammatory conditions) and critical organ failure (e.g. acute respiratory distress syndrome, cancer, hepatic and renal failure). In the U.S. the incidence of neuromorbidity ranges from 5-47% in critically ill children and adults, thus impacting hundreds of thousands of patients annually. Often neuromorbidity evolves undetected until after clinical manifestations emerge and is irreversible. Neuromorbidity can strike acutely, e.g. seizures, stroke, intracerebral hemorrhage, cerebral edema, and/or delirium, or in a more protracted fashion, e.g. neuromuscular weakness and/or cognitive decline, and is often permanent, endured throughout the remainder of a person’s lifetime. No standard clinical tool exists to identify patients at risk for neuromorbidity or for real-time neurologic monitoring, in stark contrast to the heart, kidney, liver, and many other organs. To fill this gap and transform the way clinicians detect and monitor for evolving brain injury, we developed a Bio-digital Rapid Alert to Identify Neuromorbidity (BRAIN) that continuously feeds electronic health record (EHR) variables in 9 clinical domains (A through I) into proprietary informatics and machine learning platforms. Prototype BRAIN A-I models are robust and predict clinician concern for neuromorbidity before clinical action is taken. To link biological and digital signatures, we have defined a panel of serum biomarkers that can identify time-documented neuromorbidity before clinical detection. Using a “Bayesian to Bedside” approach, we have created a live data pipeline bridging the EHR and a dedicated host server, establishing the infrastructure necessary to operationalize BRAIN A-I as an embedded predictive analytic and decision-driving support tool. In this proposal we will test the hypothesis that digital signatures in the EHR coupled with brain-specific biomarkers can rapidly detect neuromorbidity in critically ill children. Successful deployment of interoperable, 24/7 point-of-care neurologic monitoring for early detection of neuromorbidity would represent a breakthrough for the clinical management of critically ill patients.