ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention

ADAPT：自主谵妄监测和适应性预防

• 批准号: 10602426
• 负责人: Azra Bihorac
• 金额: $ 55.72万
• 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/10602426
• 关键词: Acute; 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; Frequencies; Hospital Costs; Human; Image; Immobilization; Impaired cognition; Intelligence; Intensive Care Units; Intervention; Light; Medical; Medicine; Mission; Modeling; Monitor; Neurology; Noise; Observational Study; Outcome; Pain; Patient-Focused Outcomes; Patients; 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; brain dysfunction; circadian; circadian regulation; clinical care; clinical decision-making; cost; data sharing; deep learning; deep learning algorithm; deep learning model; disease diagnosis; dynamic system; high risk; improved; innovation; light intensity; mortality; noise exposure; novel; novel strategies; patient mobility; pharmacologic; 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 最近的大规模试验没有显示出del妄的药物干预措施的显着好处 患者，非药理学方法仍然是预防ir妄的基石。其中 策略，最大程度地限制患者的不动性和昼夜节律尤其难以实施，因为 他们的评估取决于零星的人类观察。该应用程序的总体目的是 开发适应性，使用新颖的普遍性的自主del妄监测和适应性预防系统 感知和深度学习技术。它将自主量化患者的活动能力和昼夜节律 在夜间干扰，光强度和声音压力水平方面，取消性。这将允许 将这些风险因素整合到预测del妄轨迹的动态模型中。它也将启用 自适应行动促使旨在提高患者的活动能力，降低夜间干扰，优化环境 基于精确实时定量，光和降低噪声。理由是成功申请 拟议的技术将在快节奏的ICU环境中增加临床决策，并且 将促进更多针对性的干预措施。总体目标将通过追求三个特定的 目标。 （1）开发和验证可解释的深度学习算法以进行精确和动态预测 del妄轨迹的轨迹，以确定它在预测del妄轨迹过渡方面是否更准确 与现有工具相比，同时为物理提供了可解释的信息。 （2）开发 用于自动监测流动性和昼夜节律的普遍灵敏度​​系统，以确定它是否是否 与人类专家和昼夜节律生物标志物相比，可以提供准确的评估，如果可以丰富 与临床数据结合使用时，del妄轨迹预测。 （3）开发和评估提示 使用实时监控系统预防自适应ir妄，以确定该系统是否可接受 ICU医生的满意度和感知的益处。这种方法是创新的，因为它代表 首次尝试（1）动态预测精确的ir妄轨迹，（2）自主监视移动性和 ICU中的昼夜节律危险因素，以及（3）实时实施适应性预防。 拟议的研究很重要，因为它将解决重症监护的几个关键问题和关键障碍， 包括（1）缺乏精确和实时ir妄轨迹预测模型，（2） 流动性和昼夜节律脱节，以及（3）对非药物的新方法的需求 预防。最终，结果有望改善患者的预后并减少住院 成本以及终身并发症。

项目成果

Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology.

数字健康变革者和人工智能肾病学的机遇。

• DOI: 10.2215/cjn.0000000000000085
• 发表时间: 2023
• 期刊: Clinical journal of the American Society of Nephrology : CJASN
• 作者: Shickel,Benjamin;Loftus,TylerJ;Ren,Yuanfang;Rashidi,Parisa;Bihorac,Azra;Ozrazgat-Baslanti,Tezcan
• 通讯作者: Ozrazgat-Baslanti,Tezcan