ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention

ADAPT：自主谵妄监测和适应性预防

• 批准号: 10178157
• 负责人: Azra Bihorac
• 金额: $ 61.26万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10178157
• 关键词: Acute; Address; Affect; Algorithms; Biological Markers; Circadian Dysregulation; Circadian desynchrony; Clinical; Clinical Data; Clinical Informatics; Collaborations; Coma; Computing Methodologies; Critical Care; Critical Illness; Cues; Data; Data Set; Delirium; Electronic Health Record; Environment; Fostering; Foundations; Frequencies; Hospital Costs; Human; Image; Impaired cognition; Intelligence; Intensive Care Units; Intervention; Light; Medical; Medicine; Mission; Modeling; Monitor; Neurology; Noise; Observational Study; Outcome; Pain; Patient-Focused Outcomes; Patients; Pharmacology; Physicians; Physiological; Prevention; Prevention strategy; Process; Provider; Public Health; Reproducibility; Research; Risk Factors; Sampling; Sleep disturbances; Syndrome; System; Techniques; Technology; Testing; Time; United States; United States National Institutes of Health; advanced disease; base; brain dysfunction; circadian; circadian regulation; clinical care; clinical decision-making; cost; deep learning; deep learning algorithm; disease diagnosis; dynamic system; high risk; improved; innovation; light intensity; mortality; novel; novel strategies; patient mobility; predictive modeling; pressure; prospective; real time monitoring; satisfaction; sensor; sensor technology; sound; tool

Project Summary Recent large-scale trials have shown no significant benefit of pharmacological interventions in delirium patients, and non-pharmacological approaches remain the cornerstone of delirium prevention. Among those strategies, minimizing patient immobility and circadian desynchrony are particularly difficult to implement, as their assessment is dependent on sporadic human observations. The overall objective of this application is to develop ADAPT, the Autonomous Delirium Monitoring and Adaptive Prevention system using novel pervasive sensing and deep learning techniques. It will autonomously quantify patients’ mobility and circadian desynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for integration of these risk factors into a dynamic model for predicting delirium trajectories. It will also enable adaptive action prompts aimed at increasing patients’ mobility, reducing nightly disruptions, optimizing ambient light, and reducing noise, based on precise real-time quantification. The rationale is that successful application of the proposed technology would augment clinical-decision making in the fast-paced ICU environment and would promote more targeted interventions. The overall objective will be achieved by pursuing three specific aims. (1) Developing and validating an interpretable deep learning algorithm for precise and dynamic prediction of the delirium trajectory, to determine if it is more accurate in predicting delirium trajectory transitions compared to existing tools, while providing interpretable information to the physician. (2) Developing a pervasive sensing system for autonomous monitoring of mobility and circadian desynchrony, to determine if it can provide accurate assessments compared to human expert and circadian biomarkers, and if it can enrich delirium trajectory prediction when combined with clinical data. (3) Developing and evaluating prompts for adaptive delirium prevention using real-time monitoring system, to determine if the system has acceptable satisfaction and perceived benefit among ICU physicians. The approach is innovative, because it represents the first attempt to (1) dynamically predict precise delirium trajectory, (2) autonomously monitor mobility and circadian desynchrony risk factors in the ICU, and (3) implement adaptive preventions in real time. The proposed research is significant since it will address several key problems and critical barriers in critical care, including (1) lack of precise and real-time delirium trajectory prediction models, (2) uncaptured aspects of mobility and circadian desynchrony, and (3) the need for novel approaches for non-pharmacological prevention. Ultimately, the results are expected to improve patient outcomes and decrease hospitalization costs, as well as lifelong complications.

项目摘要 最近的大规模试验表明药物干预对谵妄没有明显的益处 患者，非药物方法仍然是谵妄预防的基石。人中 策略，最大限度地减少患者的不动性和昼夜节律性，特别难以实施， 它们的评估依赖于零星的人类观察。本应用程序的总体目标是 开发ADAPT，自主谵妄监测和自适应预防系统，使用新的普适 传感和深度学习技术。它将自动量化患者的活动性和昼夜节律 在夜间干扰、光强度和声压级方面，这将允许 将这些危险因素整合到预测谵妄轨迹的动态模型中。它还将使 自适应行动提示，旨在增加患者的流动性，减少夜间干扰，优化环境 基于精确的实时量化，实现更高的亮度和更低的噪音。理由是成功的应用 所提出的技术将在快节奏的ICU环境中增强临床决策， 将促进更有针对性的干预。总体目标将通过以下三个具体方面来实现： 目标。(1)开发和验证可解释的深度学习算法，用于精确和动态预测 的谵妄轨迹，以确定它是否更准确地预测谵妄轨迹转换 与现有的工具相比，同时向医生提供可解释的信息。(2)开发一 用于自主监测移动性和昼夜节律的普及感测系统，以确定其是否 与人类专家和昼夜节律生物标志物相比， 结合临床数据进行谵妄轨迹预测。(3)开发和评估提示， 自适应谵妄预防使用实时监测系统，以确定系统是否具有可接受的 ICU医生的满意度和感知效益。这种方法是创新的，因为它代表了 首次尝试（1）动态预测精确的谵妄轨迹，（2）自主监测移动性， ICU中的昼夜节律性疾病危险因素，以及（3）真实的时间实施适应性预防。的 所提出的研究是重要的，因为它将解决重症监护中的几个关键问题和关键障碍， 包括（1）缺乏精确和实时的谵妄轨迹预测模型，（2） 流动性和昼夜节律，和（3）需要新的方法，非药物 预防最终，结果有望改善患者的预后并减少住院治疗 成本，以及终身并发症。