Bio-digital Rapid Alert to Identify Neuromorbidity

识别神经疾病的生物数字快速警报

• 批准号: 10456945
• 负责人: Alicia K Au
• 金额: $ 63.04万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10456945
• 关键词: Acute; Acute Respiratory Distress Syndrome; Address; Admission activity; Adult; Age; Automobile Driving; Biochemical; Biological; Biological Assay; Biological Markers; Blinded; Brain; Brain Injuries; COVID-19; Caring; Cerebral Edema; Cerebral hemisphere hemorrhage; Cessation of life; Characteristics; Childhood; Clinical; Clinical Management; Consultations; Coupled; Critical Illness; Critically ill children; Custom; Data; Data Set; Delirium; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Elderly; Electronic Health Record; Encephalopathies; Enrollment; Epidemic; Equipment and supply inventories; Fast Healthcare Interoperability Resources; Feeds; Focus Groups; Future; Gender; Heart; Hospitals; Impaired cognition; Incidence; Infant; Inflammatory; Informatics; Infrastructure; Intensive Care Units; Intracranial Hemorrhages; Kidney; Kidney Failure; Laboratories; Link; Liver; Liver Failure; Machine Learning; Malignant Neoplasms; Mechanics; Modeling; Monitor; Morbidity - disease rate; Neurologic; Organ; Organ failure; Patients; Pediatric Hospitals; Pediatric Intensive Care Units; Performance; Persons; Pharmaceutical Preparations; Physiological; Predictive Analytics; Process; Quality of life; Race; Risk; Sampling; Seizures; Sepsis; Serum; Stroke; Testing; Time; Validation; Virus Diseases; base; biomarker signature; clinical diagnosis; cohort; data pipeline; digital; functional status; interoperability; machine learning method; neonate; neuromuscular; neurotropic; point of care; prevent; prospective; prototype; side effect; specific biomarkers; support tools; tool; usability; vector

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.

住院医师的神经系统杂种或神经粒性的沉默发展和进展， 重症患者代表了新认可和新兴的流行病。这包括接受的患者 具有主要神经系统诊断的重症监护病房，基于其潜在的风险增加 疾病（例如神经性病毒感染，包括COVID19），以及那些发展的疾病 神经形成是神秘的，意外的。与危害疾病相关的神经形成可能是由 生理不稳定性，生化进化，药物的副作用，侵入性机械支持， 用于预防死亡的固定性和/或其他疗法。它跨越了从新生儿到新生儿的年龄频谱 老年人，发生在性别和种族之间，在全身性疾病的患者中被识别得很低（例如败血症， 病毒感染和其他炎症状况）和严重器官衰竭（例如急性呼吸窘迫 综合征，癌症，肝脏和肾衰竭）。在美国，神经病的发病率从5-47％ 重症儿童和成人重病，因此每年影响数十万患者。经常 神经形成的演变才被发现直到临床表现出现后，并且是不可逆的。 可以敏锐地撞击，例如癫痫发作，中风，脑内出血，脑水肿和/或del妄，或 更持久的时尚，例如神经肌肉无力和/或认知能力下降，通常是永久的， 在一个人一生的剩余时间里忍受。没有标准临床工具可以识别患者 与心脏，肾脏，肝脏和实时神经系统监测的神经症或实时神经系统监测的风险 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控脑损伤的方式，我们开发了 生物数字快速警报，以识别连续喂养电子健康记录的神经形状（大脑） （EHR）9个临床领域（a至i）中的变量（a）变成专有信息和机器学习平台。 原型大脑A-I模型是强大的，并且在临床作用之前预测神经杂菌的临床关注 带。为了连接生物学和数字签名，我们定义了一组血清生物标志物，可以识别 临床检测前有时间文献的神经虫。使用“贝叶斯到床边”方法，我们有 创建了一个实时数据管道，桥接EHR和专用主机服务器，建立基础架构 将大脑A-I作为嵌入式预测分析和决策驱动支持工具进行操作所必需的。 在此提案中，我们将检验以下假设：EHR中的数字签名与大脑特异性 生物标志物可以迅速检测到重症儿童的神经病。成功部署可互操作的部署， 24/7点神经系统监测以早期检测到神经虫的疾病将代表突破 用于重症患者的临床管理。