Emergency Neurophysiological Assessment Bedside Logic Engine

紧急神经生理学评估床边逻辑引擎

• 批准号: 7828395
• 负责人: Don M Tucker
• 金额: $ 95.47万
• 依托单位: ELECTRICAL GEODESICS, INC.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7828395
• 关键词: Accident and Emergency department; Adoption; Algorithms; Automated Pattern Recognition; Behavior; Blinking; Brain; Brain Injuries; Cardiac; Cephalic; Clinical; Clinical Data; Clinical Informatics; Coma; Complex; Computer software; Consensus; Consultations; Craniocerebral Trauma; Critical Care; Data; Databases; Decision Analysis; Detection; Diagnosis; Diagnostic; Disease; Drug Formulations; Drug toxicity; Emergency Situation; Equipment; Evaluation; Excision; Failure; Family; Generations; Goals; Guidelines; Hair; Health Personnel; Health Sciences; Hematoma; High Performance Computing; Hour; Human Resources; Informatics; Injury; Intensive Care; Intervention; Jordan; Knowledge; Learning; Length; Link; Literature; Logic; Measures; Medical; Medical Records; Medical Technology; Methods; Monitor; Morphologic artifacts; Movement; Nervous System Trauma; Neurologic; Neurological emergencies; Neurologist; Neurology; Noise; Nurses; Ontology; Oregon; Pathology; Patient Care; Patients; Pattern; Pattern Recognition; Physician Assistants; Physicians; Physiologic Monitoring; Plague; Population; Process; Protocols documentation; Reading; Risk; Seizures; Signal Transduction; Societies; Source; Staging; Status Epilepticus; Symptoms; Syndrome; System; Taxonomy; Technology; Testing; Time; Training; Universities; Vasospasm; Visual; Widespread Disease; Work; clinical decision-making; clinically significant; computerized; cost; emergency service responder; evidence base; experience; improved; meetings; nervous system disorder; neurological pathology; neuropathology; neurophysiology; new technology; operation; programs; public health relevance; relating to nervous system; repository; research and development; sensor; simulation; text searching; tool; usability

DESCRIPTION (provided by applicant): Emergency Neurophysiological Assessment Bedside Logic Engine We propose to develop an intelligent clinical informatics search tool that is integrated with automated brain monitoring with dense array EEG (dEEG; 128 or 256 channels). Many forms of neurological injury, such as hematoma or nonconvulsive seizure, are difficult to diagnose in the emergency room, and yet this is the point at which they are often most effectively treated (Jordan, 1999). Failure to recognize treatable brain injury leads to extensive suffering for patients and families and costs to society, such that an inexpensive automated brain monitor could be highly cost-effective in the emergency context. Continuous monitoring of brain function can now be accomplished easily and inexpensively in the emergency setting with dEEG (Luu, et al., 2001), providing key neurophysiological information for emergency neurological assessment (Jordan, 1999, Procaccio et al., 2001). There are, however, both technical and professional challenges to make dEEG monitoring practical for routine use. Technically, continuous monitoring is required for many neurological conditions, and this is not practical with visual inspection of EEG traces or crude automated measures such as integrated amplitude. Reliable automated pattern recognition is required, yet in the past this has often failed to separate real neurological disorder from artifacts such as movement, eye blinks, and cardiac signals. There are also professional challenges, one in acquiring the brain data and another in interpreting it. First, EEG technologists are often not available in the emergency department, so that EEG sensor application must be easy enough for nurses and medical assistants who have completed a short, focused training protocol. Second, emergency physicians are not trained in EEG interpretation so they require assistance. Optimally, this would both access to consultation by an expert neurologist and an intelligent search engine that reads the dEEG pattern results and provides an evidence-based interpretation in relation to diagnostic questions to be evaluated. We propose a two-year research and development program to meet both these technical and professional challenges. We will begin with a intelligent search tool, the Clinical Decision Analysis (CDA) system from Lifecom, Inc. that has been proven effective for providing online guidance to a physician or physician's assistant in the emergency department. The CDA helps with evaluating symptoms in the context of medical evidence, hypothesizing disease states, ordering lab tests, and making diagnostic and treatment decisions. An extensive database of clinical evidence and guidelines is made available at the bedside, with contextual search tools that track the stages of evidence-gathering and clinical decisions required for emergency evaluation. We will develop a specialized version of the CDA for evaluation of emergent neurological disorders, and we will provide evidence on neural status from online monitoring of dEEG. Rapid (one minute) application of the dEEG sensor net by the first responder allows monitoring to begin when the patient is first contacted. Pattern recognition with the dEEG data is provided by high performance compute clusters: (1) to separate brain signals from noise (e.g., movement, cardiac, equipment artifacts) and (2) to recognize pathological brain states (e.g,. seizure, burst-suppression coma, drug toxicity, vasospasm, focal slowing due to hematoma). The fusion of automated neurophysiological monitoring with the intelligent diagnostic search tool will create the Emergency Neurophysiological Assessment Bedside Logic Engine (ENABLE). ENABLE can be seen as a new paradigm in which informatics allows a two-way exchange between clinical data gathering and clinical data interpretation. In supporting clinical data gathering, the knowledge assembled by an intelligent search tool (such as the clinical presentation of a patient with mild head trauma, together with expected complications such as hematoma) is used to provide a predictive analysis of the brain monitoring data, highlighting the patterns that may be most diagnostic in that context (such as the progression of regional EEG slowing that may signal a developing hematoma). Thus, the difficult challenge of pattern recognition is made easier by placing it in the continually developing diagnostic context framed by the informatics logic engine. The improved pattern recognition of neuropathology then paves the way to improved clinical decision making. Through the fusion of automated neurophysiological pattern recognition, ENABLE not only summarizes the patient's brain state for the physician, but it presents a set of rule-out and rule-in protocols that allow active hypothesis-testing, and additional data gathering, to inform the diagnostic decision. Instead of an intelligent search tool that begins only with the physician's queries and observations, ENABLE can act in the background to evaluate the patient's ongoing changes in neurophysiological state and to assemble an intelligent set of diagnostic options that are consistent with those changes. These are tested against the additional diagnostic and clinical data available on that patient, such that ENABLE can then set alarms, launch literature searches, suggest additional tests, and otherwise assist the diagnostic and treatment processes. Building ENABLE requires extending the CDA Knowledge Repository (KR) to recognize neurophysiological pathology in the dEEG signals and to link this pathology to neurological disease states and diagnostic syndromes. Our neurology consultant, Dr. Mark Holmes, is a clinical neurophysiologist who will work with Dr. Datena and other emergency physicians to extend the CDA KR to emergent neurological conditions. Given the capture and formulation of this clinical knowledge within the KR, we will turn to a key goal for the widespread adoption of ENABLE: a program of simulations that train medical personnel in emergency neurological evaluation, including integrated dEEG pattern recognition when this is available. Through this integration, ENABLE will offer the opportunity for a new form of intelligent clinical search tool that is linked directly to an advanced technology for automated physiological monitoring. PUBLIC HEALTH RELEVANCE: This project would create an advanced computing technology for continuous brain monitoring in emergency and intensive care settings. A brain wave sensor net allows rapid application of a dense array of electroencephalographic (EEG) sensors with elementary training. Advanced computation methods allow automated detection of seizures and other forms of injury that put the brain at risk. The complete system will be integrated with clinical decision assistance software that will provide emergency physicians with guidelines for incorporating the brain status information into immediate medical decisions, and for seeking expert neurological review when necessary. Network access technology will facilitate remote review by expert neurologists, and a training protocol will provide first-responder usability for emergency technicians and physicians without training in clinical neurophysiology.

项目描述（申请人提供）：紧急神经生理评估床边逻辑引擎我们拟开发一种智能临床信息学搜索工具，该工具与密集阵列脑电图（dEEG； 128或256通道）自动脑监测相结合。许多形式的神经损伤，如血肿或非惊厥性发作，在急诊室很难诊断，但这一点往往是最有效的治疗（Jordan, 1999）。未能识别可治疗的脑损伤会给患者和家属带来广泛的痛苦，并给社会带来成本，因此，在紧急情况下，廉价的自动脑监测仪可能具有很高的成本效益。现在，在紧急情况下，使用dEEG可以轻松而廉价地完成对脑功能的持续监测（Luu等，2001年），为紧急神经评估提供关键的神经生理学信息（Jordan， 1999年，Procaccio等，2001年）。然而，要使dEEG监测实际用于日常使用，在技术和专业方面都存在挑战。从技术上讲，许多神经系统疾病都需要连续监测，这对于脑电图痕迹的目视检查或综合振幅等粗糙的自动化测量是不切实际的。可靠的自动模式识别是必需的，但在过去，这通常无法将真正的神经系统疾病与诸如运动、眨眼和心脏信号等伪影区分开。还有专业上的挑战，一个是获取大脑数据，另一个是解释数据。首先，急诊科通常没有脑电图技术人员，因此脑电图传感器的应用对于完成了短期重点培训协议的护士和医疗助理来说必须足够容易。第二，急诊医生没有接受过脑电图解释方面的培训，因此他们需要帮助。最理想的情况是，这既可以获得神经科专家的咨询，也可以获得读取dEEG模式结果的智能搜索引擎，并为待评估的诊断问题提供基于证据的解释。我们提出了一项为期两年的研究和发展计划，以应对这些技术和专业挑战。我们将从一个智能搜索工具开始，即来自Lifecom公司的临床决策分析（CDA）系统，该系统已被证明可以有效地为急诊科的医生或医生助理提供在线指导。CDA有助于在医学证据的背景下评估症状、假设疾病状态、订购实验室检查以及做出诊断和治疗决定。床边有一个广泛的临床证据和指南数据库，配有上下文搜索工具，可跟踪证据收集的各个阶段和紧急评估所需的临床决策。我们将开发一个专门版本的CDA来评估紧急神经系统疾病，我们将从dEEG的在线监测中提供神经状态的证据。第一响应者快速（一分钟）应用dEEG传感器网，可以在患者第一次接触时开始监测。dEEG数据的模式识别由高性能计算集群提供：(1)将大脑信号与噪声（例如，运动、心脏、设备伪影）分离；(2)识别病理大脑状态（例如，运动、心脏、设备伪影）。癫痫发作，突发抑制昏迷，药物毒性，血管痉挛，因血肿引起的局灶性减缓)。自动神经生理监测与智能诊断搜索工具的融合将创建紧急神经生理评估床边逻辑引擎（ENABLE）。ENABLE可以被视为一种新的范例，其中信息学允许临床数据收集和临床数据解释之间的双向交换。在支持临床数据收集方面，通过智能搜索工具收集的知识（例如轻度头部创伤患者的临床表现，以及血肿等预期并发症）用于提供脑监测数据的预测性分析，突出显示在该背景下可能最具诊断性的模式（例如可能表明血肿发展的区域脑电图减慢的进展）。因此，通过将模式识别置于信息学逻辑引擎框架下不断发展的诊断环境中，模式识别的困难挑战变得更加容易。神经病理学模式识别的改进为临床决策的改进铺平了道路。通过自动神经生理模式识别的融合，ENABLE不仅为医生总结了患者的大脑状态，而且还提供了一套排除和规则纳入的协议，允许积极的假设检验和额外的数据收集，以告知诊断决策。与仅从医生的询问和观察开始的智能搜索工具不同，ENABLE可以在后台评估患者神经生理状态的持续变化，并根据这些变化组装一组智能诊断选项。这些测试将根据该患者的其他诊断和临床数据进行测试，这样ENABLE就可以设置警报，启动文献搜索，建议额外的测试，并以其他方式协助诊断和治疗过程。建立ENABLE需要扩展CDA知识库（KR），以识别dEEG信号中的神经生理病理，并将这种病理与神经疾病状态和诊断综合征联系起来。我们的神经病学顾问Mark Holmes博士是一名临床神经生理学家，他将与Datena博士和其他急诊医生合作，将CDA KR扩展到紧急神经系统疾病。考虑到在KR中捕获和形成这种临床知识，我们将转向广泛采用ENABLE的关键目标：一个模拟程序，用于培训医疗人员进行紧急神经学评估，包括在可用时集成dEEG模式识别。通过这种整合，ENABLE将为一种新型的智能临床搜索工具提供机会，该工具与一种先进的自动生理监测技术直接相连。