ICP Elevation Alerting Based on a Predictive Model Hosting Platform

基于预测模型托管平台的 ICP 海拔警报

• 批准号: 8222233
• 负责人: Xiao Hu
• 金额: $ 42.95万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8222233
• 关键词: Acute; Adopted; Adoption; Algorithms; Attention; Blood flow; Brain; Brain Injuries; Caring; Cerebral Ischemia; Clinical Data; Clinical Decision Support Systems; Computer software; Computerized Medical Record; Data; Data Set; Detection; Development; Discipline of Nursing; Engineering; Evaluation; Event; Future; Goals; Hour; Human; Intensive Care; Intracranial Hypertension; Intracranial Pressure; Investigation; Lead; Machine Learning; Manuals; Metric; Mining; Modeling; Monitor; Morphologic artifacts; Nature; Nurses; Patients; Pattern; Physiologic pulse; Physiological; Play; Plumbing; Process; Protocols documentation; Recording of previous events; Rest; Signal Transduction; System; Techniques; Testing; Time; Translating; Translations; Trust; Work; base; clinical application; clinical decision-making; cost; design; improved; innovation; interest; open source; point of care; predictive modeling; response; usability

DESCRIPTION (provided by applicant): Recurring acute ICP elevations occur frequently and unpredictably among severe brain injury patients. ICP elevation can cause cerebral ischemia and lead to deadly brain herniation if untreated. Hence, prompt recognition and treatment of rising ICP are critical in managing severe brain injury patients. However, existing protocols in most neurocritical care units are reactive where bedside nurses, in response to simple threshold-crossing alarms, have to check numerical display of ICP on monitors to manually establish whether the alarm is a true one before initiating treatment. Acute ICP elevation is accompanied by distinctive ICP pulse morphological changes. By utilizing ICP pulse morphological metrics as input, we can accurately recognize precursors to ICP elevation to alert nurses and free them from a cognitively demanding process of establishing whether a consistent ICP elevation triggers the alarm. We therefore propose to deploy a previously developed accurate ICP elevation prediction model on an open-source model hosting platform to monitor continuous ICP signals and alert bedside nurses. Using this alerting system, we will further investigate the principal physiological abnormalities associated with acute ICP elevation showing different precursory ICP patterns prior to onset of elevation. We will pursue the following three aims: 1) To develop an alerting system for ICP elevation based on a model hosting platform; 2) To investigate whether the ICP alerting system helps nurses more efficiently manage ICP. 3) To detect consistent physiological abnormalities associated with acute ICP elevation. Our long-term goal is to advance intensive care monitoring so that continuous signals from monitors are fully explored to integrate with the rest of clinical data in an electronic medical record (EMR) system to enhance clinical decision making. This project represents an effort piloting a platform-based approach towards overcoming translational barriers that impede the process of making advanced predictive analytics available at point of care. Therefore, broad impacts from this project are related to future efforts at leveraging this open model hosting platform to facilitate the translation of additional predictive models in other ICUs. PUBLIC HEALTH RELEVANCE: Recurring acute intracranial pressure (ICP) elevation occurs frequently, up to more than 20 in a 12-hour nursing shift, and unpredictably among severe brain injury patients. These acute ICP elevations needs prompt treatment before they impair blood flow to the brain and cause deadly brain herniation. The present work is built upon our previously developed algorithm of detecting acute ICP elevation and an open software platform for hosting predictive algorithms to further develop and evaluate a real-time ICP elevation alerting system that will provide accurate alerts of impending ICP elevation. Rigorous human factor engineering principles and techniques will be adopted in developing this system and we will further leverage this real-time alerting system to investigate the principal physiological abnormalities that are associated with accurate ICP elevation.

描述（由申请人提供）：在严重脑损伤患者中，反复出现急性颅内压升高是经常发生且不可预测的。如果不及时治疗，颅内压升高可引起脑缺血并导致致命的脑疝。因此，及时识别和治疗颅内压升高是处理重型颅脑损伤患者的关键。然而，大多数神经危重症监护病房的现有方案是被动的，床边护士在对简单的阈值越过警报作出反应时，必须在开始治疗之前检查监视器上的ICP数字显示，以手动确定警报是否真实。急性颅内压升高伴有明显的颅内压脉冲形态学改变。通过使用ICP脉冲形态学指标作为输入，我们可以准确地识别ICP升高的前兆，以提醒护士，并将他们从建立一致的ICP升高是否触发警报的认知要求过程中解放出来。因此，我们建议在开源模型托管平台上部署先前开发的精确ICP升高预测模型，以监测持续的ICP信号并提醒床边护士。使用这个警报系统，我们将进一步研究与急性ICP升高相关的主要生理异常，在升高发生之前显示不同的先兆ICP模式。我们将追求以下三个目标：1)开发基于模型托管平台的ICP标高预警系统；2)探讨ICP报警系统是否能帮助护士更有效地管理ICP。3)检测与急性ICP升高相关的一致生理异常。我们的长期目标是推进重症监护监测，以便充分利用监视器发出的连续信号，将其他临床数据整合到电子病历（EMR）系统中，以增强临床决策。该项目代表了一种基于平台的方法的试点工作，旨在克服阻碍在护理点提供高级预测分析过程的翻译障碍。因此，这个项目的广泛影响与未来利用这个开放模型托管平台来促进其他ci中其他预测模型的翻译的努力有关。