ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention

项目摘要 最近的大规模试验表明，药物干预对神志不清没有明显的益处。 患者和非药物方法仍然是预防精神错乱的基石。其中包括 最大限度地减少患者不动和昼夜节律不同步的策略尤其难以实施，因为 他们的评估依赖于零星的人类观察。此应用程序的总体目标是 使用新型普及性开发ADAPT，自主的妄想症监测和自适应预防系统 感知和深度学习技术。它将自动量化患者的活动性和昼夜节律 在夜间中断、光线强度和声压级方面的去同步。这将允许 将这些危险因素整合到一个动态模型中，以预测精神错乱的轨迹。它还将启用 旨在提高患者机动性、减少夜间干扰、优化环境的自适应行动提示 在精确的实时量化的基础上，实现了轻量化和降噪。其基本原理是成功申请 建议的技术将增强快节奏ICU环境中的临床决策能力，并 将促进更有针对性的干预。总体目标将通过实现三个具体目标来实现 目标。(1)开发并验证了一种可解释的深度学习算法，用于精确和动态预测 以确定它在预测精神错乱轨迹转变方面是否更准确 与现有工具相比，同时向医生提供可解释的信息。(2)发展 用于自主监测移动性和昼夜去同步性的普及传感系统，以确定它是否 与人类专家和昼夜节律生物标记物相比，可以提供准确的评估，如果它能丰富 结合临床资料预测妄想轨迹。(3)制定和评估提示 采用自适应防止妄想的实时监测系统，以确定该系统是否有可接受 ICU医生的满意度和感知利益。这种方法是创新的，因为它代表了 首次尝试(1)动态预测精确的精神错乱轨迹，(2)自主监测活动和 在ICU中发现昼夜节律不同步的危险因素，以及(3)实时实施适应性预防。这个 拟议的研究具有重要意义，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)缺乏准确和实时的精神错乱轨迹预测模型，(2)未捕捉到的方面 流动性和昼夜节律去同步性，以及(3)需要新的非药理学方法 预防。最终，这些结果有望改善患者的预后，减少住院时间。 成本，以及终生的并发症。

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.