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: 8466387
• 负责人: Xiao Hu
• 金额: $ 22.72万
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8466387
• 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.

描述（由申请人提供）：严重脑损伤患者中频繁且不可预测地反复发生急性ICP升高。如果不及时治疗，ICP 升高会导致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对于治疗严重脑损伤患者至关重要。然而，大多数神经重症监护病房的现有方案都是反应性的，床边护士为了响应简单的跨越阈值警报，必须检查监视器上 ICP 的数字显示，以在开始治疗之前手动确定警报是否真实。急性 ICP 升高伴随着独特的 ICP 脉冲形态变化。通过利用 ICP 脉冲形态指标作为输入，我们可以准确识别 ICP 升高的前兆，以向护士发出警报，并使他们从确定一致的 ICP 升高是否触发警报的认知要求较高的过程中解放出来。因此，我们建议在开源模型托管平台上部署先前开发的准确 ICP 升高预测模型，以监测连续 ICP 信号并提醒床边护士。使用该警报系统，我们将进一步研究与急性 ICP 升高相关的主要生理异常，显示升高发生之前的不同前兆 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升高预警系统； 2) 调查ICP警报系统是否可以帮助护士更有效地管理ICP。 3) 检测与急性ICP升高相关的一致的生理异常。 我们的长期目标是推进重症监护监护，以便充分探索监护仪的连续信号，与电子病历 (EMR) 系统中的其他临床数据集成，从而增强临床决策。该项目代表了一项尝试基于平台的方法的努力，旨在克服阻碍在护理点提供高级预测分析过程的转化障碍。因此，该项目的广泛影响与未来利用该开放模型托管平台促进其他 ICU 中其他预测模型的转换的努力有关。