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.

描述（由申请人提供）：紧急神经生理评估床边逻辑引擎，我们建议开发一种智能的临床信息搜索工具，该工具与密集阵列EEG（DEEG; 128或256个频道）集成了自动化的大脑监测。在急诊室很难诊断出许多形式的神经系统损伤，例如血肿或非脱弹性癫痫发作，但这通常是最有效治疗的点（Jordan，1999）。无法识别可治疗的脑损伤会给患者和家庭带来严重的痛苦，并使社会成本造成了廉价的自动化脑监测器，在紧急情况下可能具有很高的成本效益。现在，在DEEG（Luu等，2001）的紧急情况下，可以轻松，廉价地对大脑功能进行连续监测，从而为紧急神经学评估提供关键的神经生理信息（Jordan，1999； Procaccio等，2001）。但是，有技术和专业的挑战，可以使DEEG监控进行常规使用。从技术上讲，对于许多神经系统条件，需要连续监测，这对于脑电图痕迹或原始自动化措施（例如综合振幅）的目视检查是不可行的。需要可靠的自动化模式识别，但是过去，这通常无法将真实的神经系统疾病与运动，眼睛眨眼和心脏信号等人工制品分开。也存在专业挑战，一个是获取大脑数据，另一个是解释它。首先，急诊室通常不可用脑电图技术人员，因此对于完成了简短而重点的培训协议的护士和医疗助理，EEG传感器的应用必须足够容易。其次，急诊医师未接受脑电图解释的培训，因此需要帮助。最佳地，这既可以访问专家神经科医生的咨询，又是智能搜索引擎，读取DEEG模式结果，并提供了与要评估的诊断问题有关的基于证据的解释。我们提出了一项为期两年的研发计划，以应对这些技术和专业挑战。我们将从Lifecom，Inc。的智能搜索工具，即临床决策分析（CDA）系统开始，该系统已被证明可有效地向急诊室的医师或医师助理提供在线指导。 CDA有助于在医学证据，假设的疾病状态，订购实验室测试以及做出诊断和治疗决策的情况下评估症状。在床边提供了广泛的临床证据和准则数据库，并提供了背景搜索工具，可以跟踪循证收集和紧急评估所需的临床决策阶段。我们将开发一个专门的CDA版本，以评估新兴的神经系统疾病，并将提供有关DEEG在线监测的神经状况的证据。第一响应者对DEEG传感器网的快速应用允许在首次联系患者时开始监测。使用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，设备伪像）和（2）识别病理脑状态（例如，癫痫发作，癫痫发作，抗抑郁症，药物毒性，药物毒性，血管痉挛，血管痉挛，焦点缓慢）。自动化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）。启用可以看作是一种新的范式，其中信息学可以在临床数据收集和临床数据解释之间进行双向交换。在支持临床数据收集时，使用智能搜索工具组装的知识（例如，轻度头部创伤患者的临床表现以及预期的并发症（例如血肿）来提供对大脑监测数据的预测分析，突出显示在这种情况下可能是最诊断的模式（例如，在这种情况下可能是e​​ge eeg slay slay slay slay slabers slay slabers slabers slabers shegressaime shematoma shematoma shematoma shematoma shematoma shematoma shematoma shemaToma shematoma shematoma shemaToma shemaT a shemats a shemats a shematsoma的疾病。因此，将模式识别的困难挑战更加容易，将其放置在不断开发的诊断环境中，由信息学逻辑引擎构建。然后，改善神经病理学的模式识别为改善临床决策铺平了道路。通过融合自动化的神经生理模式识别，不仅可以总结医生的大脑状态，而且还提供了一套规则和规则中的规程，允许主动假设测试以及其他数据收集，以告知诊断决策。 Enable可以在背景下采取行动来评估患者在神经生理状态的持续变化，并组装一套与这些更改一致的智能诊断选择，而不是仅从医生的查询和观察开始，而不是仅从医生的查询和观察开始的智能搜索工具，而不是智能的搜索工具。根据该患者可用的其他诊断和临床数据对这些进行测试，因此启用可以设置警报，启动文献搜索，建议其他测试，并在其他方面有助于诊断和治疗过程。构建启用需要扩展CDA知识存储库（KR）才能识别DEEG信号中的神经生理病理学，并将这种病理学与神经系统疾病状态和诊断综合症联系起来。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扩展到新兴的神经系统状况。鉴于KR中这种临床知识的捕获和表述，我们将朝着广泛采用Enable的关键目标：一个模拟计划，该计划在紧急神经系统评估中训练医务人员，包括在可用时进行综合的DEEG模式识别。通过这种集成，Enable将为一种新形式的智能临床搜索工具提供机会，该工具直接链接到用于自动化生理监测的先进技术。 公共卫生相关性：该项目将创建一种高级计算技术，用于在紧急和重症监护环境中连续大脑监测。脑波传感器网允许通过基本训练快速应用一系列密集的脑电图（EEG）传感器。先进的计算方法允许自动检测癫痫发作和其他形式的损伤，使大脑处于危险之中。完整的系统将与临床决策援助软件集成，该软件将为紧急医生提供将大脑状况信息纳入即时医疗决策的准则，并在必要时寻求专家神经审查。网络访问技术将促进专家神经科医生的远程审查，而培训方案将为紧急技术人员和医生提供第一响应者的可用性，而无需临床神经生理学培训。