Noncontact Remote Monitoring for the Detection of Opioid-Induced Respiratory Depression

非接触式远程监测检测阿片类药物引起的呼吸抑制

• 批准号: 10684530
• 负责人: Behnood Gholami
• 金额: $ 32.5万
• 依托单位: AUTONOMOUS HEALTHCARE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684530
• 关键词: Age; Anesthesia procedures; Area; COVID-19 pandemic; Chest; Chest wall structure; Clothing; Complement; Consumption; Data; Detection; Devices; Dose; Drug Prescriptions; Family suidae; Goals; Home; Human; Image; Injections; Involutional Depression; Measures; Medical Device; Modeling; Monitor; Movement; Opioid; Outcome; Patient Monitoring; Patients; Pattern; Penetration; Persons; Pharmaceutical Preparations; Phase; Publishing; Radar; Respiration; Sensitivity and Specificity; Signal Transduction; Site; System; Technology; Testing; Tidal Volume; Validation; Ventilatory Depression; Work; chronic pain patient; data integration; design; detection platform; healthy volunteer; image guided; imaging system; machine learning framework; machine learning model; multimodality; non-invasive monitor; opioid epidemic; opioid use disorder; optical imaging; overdose death; prescription opioid; privacy preservation; prototype; remote monitoring; research clinical testing; respiratory; sensor; statistics

The US opioid crisis continues to have a catastrophic impact on human lives and the ongoing COVID-19 pandemic is compounding its effects. Based on the statistics published by the CDC, 91,799 drug overdose deaths occurred in the US in 2020, where the age-adjusted overdose deaths increased by 31% from 2019 to 2020. In addition, opioids, which cause respiratory depression, were involved in 75% of all drug overdose deaths in the US. We propose to build on our work in non-invasive monitoring of vital signs to develop an FDA- regulated medical device with a primary application in monitoring patients for opioid-induced respiratory depression. This includes at-home monitoring of patients with chronic pain being treated with high-dose opioid prescription medications or patients suffering from opioid use disorder (OUD) as well as monitoring subjects with OUD at supervised injection sites (also known as supervised consumption spaces). Our overall goal is to develop a non-contact multi-modal monitoring system for the detection of opioid-induced respiratory depression at home and in supervised injection sites. While radar is capable of penetrating through clothing and blankets to measure chest wall movements resulting from respiration, it requires the guidance of depth imaging to target a person and the chest area. Our specific aims are: 1. Estimate tidal volume using a noncontact monitoring system. Our current technology is capable of detecting respiratory rate with a high degree of accuracy for stationary subjects. However, robust detection of respiratory depression involves monitoring of respiratory rate, pattern, and depth (i.e., tidal volume). As part of this specific aim, we will develop a framework to estimate tidal volume of a stationary subject using radar and depth information, where we estimate tidal volume from chest wall displacements. Furthermore, we will extract features to characterize respiratory pattern from the acquired radar signal. As a primary validation of this estimation framework, our system will be tested on 20 healthy volunteers. The outcome of the test will provide us with preliminary data regarding the accuracy of the radar and the depth-based tidal volume estimation as compared with the gold standard. 2. Develop and validate a framework for integrating data from sensors to detect respiratory depression. In this specific aim, we will develop a framework to use the respiratory rate, respiratory pattern, and tidal volume information from the radar and depth camera to determine if respiratory depression has occurred. This involves a two-step approach, where we extract respiratory features to characterize respiratory patterns to complement respiratory rate and tidal volume, and then use a machine learning model to detect the occurrence of respiratory depression. To help with design the right model, we will collect data using our radar and depth imaging system from anesthetized pigs going through opioid-induced respiratory depression.

美国阿片类药物危机继续对人类生命和持续的COVID-19产生灾难性影响 大流行正在加剧其影响。根据CDC发布的统计数据，91，799例药物过量 2020年，美国发生了死亡事件，其中年龄调整的过量死亡人数从2019年增加了31%， 2020.此外，导致呼吸抑制的阿片类药物占所有药物过量的75 死亡在美国。我们建议在非侵入性生命体征监测工作的基础上，制定一项FDA- 主要用于监测阿片类药物引起的呼吸的受管制医疗装置 萧条这包括对接受高剂量阿片类药物治疗的慢性疼痛患者进行家庭监测 处方药或患有阿片类药物使用障碍（OUD）的患者以及监测受试者 在受监督的注射场所（也称为受监督的消费场所）使用OUD。我们的总体目标是 开发一种非接触式多模式监测系统，用于检测阿片类药物引起的呼吸道疾病， 抑郁症在家里和监督注射部位。虽然雷达能够穿透衣服， 和毯子来测量由呼吸引起的胸壁运动，它需要深度的指导 成像以瞄准人和胸部区域。我们的具体目标是：1.使用以下方法估计潮气量： 非接触式监控系统我们目前的技术能够检测呼吸频率， 对静止物体的准确度。然而，呼吸抑制的稳健检测涉及 监测呼吸速率、模式和深度（即，潮气量）。作为这一具体目标的一部分，我们将开发 使用雷达和深度信息来估计静止对象的潮气量的框架，其中我们 从胸壁位移估计潮气量。此外，我们将提取特征来表征 从获取的雷达信号中提取呼吸模式。作为对这一估算框架的初步验证， 该系统将在20名健康志愿者身上进行测试。测试结果将为我们提供初步数据 关于雷达和基于深度的潮汐量估算的准确性， 标准2.开发并验证用于整合传感器数据的框架，以检测呼吸 萧条在这个特定的目标，我们将开发一个框架，使用呼吸频率，呼吸模式， 以及来自雷达和深度相机的潮气量信息，以确定呼吸抑制是否已经 发生了。这涉及两步方法，其中我们提取呼吸特征以表征呼吸 模式来补充呼吸频率和潮气量，然后使用机器学习模型来检测 发生呼吸抑制。为了帮助设计正确的模型，我们将使用雷达收集数据 和深度成像系统，从麻醉的猪经历阿片类药物诱导的呼吸抑制。