ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention

ADAPT：自主谵妄监测和适应性预防

基本信息

批准号：
10396041
负责人：
Azra Bihorac
金额：
$ 59.9万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
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/10396041
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 deep learning model disease diagnosis dynamic system high risk improved innovation light intensity mortality noise exposure novel novel strategies patient mobility predictive modeling pressure prospective real time monitoring satisfaction sensor sensor technology sound tool

项目摘要

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.

项目概要最近的大规模试验表明药物干预对谵妄没有显着益处其中，非药物方法仍然是预防谵妄的基石。尽量减少患者不动和昼夜节律不同步的策略尤其难以实施，因为他们的评估取决于零星的人类观察。该应用程序的总体目标是使用新颖的普遍技术开发 ADAPT，即自主谵妄监测和适应性预防系统它将自动量化患者的活动能力和昼夜节律。夜间干扰、光强度和声压级方面的不同步这将允许。将这些风险因素整合到预测谵妄轨迹的动态模型中也将成为可能。适应性行动提示旨在增加患者的活动能力、减少夜间干扰、优化环境光，并减少噪音，基于精确的实时量化的成功应用。所提出的技术将增强快节奏的 ICU 环境中的临床决策，将促进更有针对性的干预措施，通过实施三项具体措施来实现总体目标。目标 (1) 开发和验证可解释的深度学习算法，以进行精确和动态的预测。谵妄轨迹，以确定它在预测谵妄轨迹转变方面是否更准确 (2) 开发用于自主监测移动性和昼夜节律不同步的普遍传感系统，以确定它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，以及是否可以丰富结合临床数据进行谵妄轨迹预测 (3) 制定和评估提示。使用实时监控系统进行自适应谵妄预防，以确定系统是否具有可接受的性能 ICU 医生的满意度和感知效益该方法具有创新性，因为它代表了这一点。首次尝试 (1) 动态预测精确的谵妄轨迹，(2) 自主监控移动性和 ICU 中昼夜节律不同步的危险因素，以及 (3) 实时实施适应性预防。拟议的研究意义重大，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）未捕获的方面流动性和昼夜节律不同步，以及（3）需要新的非药物方法最终，结果预计将改善患者的治疗效果并减少住院率。费用以及终生并发症。