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

基本信息

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
项目类别：
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

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

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项目摘要

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的生活产生灾难性的影响，而持续的covid-19 大流行正在加剧其影响。根据CDC发布的统计数据，药物过量91,799 死亡发生在2020年美国发生，从2019年开始，年龄习惯的过量死亡人数增加了31％ 2020年。此外，阿片类药物引起呼吸抑郁症，涉及所有药物过量的75％在美国死亡。我们建议在非侵入性监测生命体征的非侵入性监测中建立工作，以开发FDA- 受监管的医疗装置，主要应用在监测患者的阿片类药物诱导的呼吸道沮丧。这包括对高剂量阿片类药物治疗慢性疼痛患者的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监督注射地点（也称为有监督的消费空间）。我们的总体目标是开发一个非接触式多模式监测系统，用于检测阿片类药物诱导的呼吸系统在家和监督注射部位的抑郁症。雷达能够穿透衣服和毯子测量呼吸引起的胸壁移动，需要深度指导成像以针对一个人和胸部区域。我们的具体目的是：1。使用非接触监测系统。我们当前的技术能够检测高的呼吸率固定受试者的准确度。但是，强大的呼吸抑郁症检测涉及监测呼吸频率，模式和深度（即潮汐体积）。作为这个特定目标的一部分，我们将发展使用雷达和深度信息估算固定主体的潮汐量的框架，我们估算胸壁位移的潮汐体积。此外，我们将提取特征以表征获得的雷达信号的呼吸模式。作为对此估计框架的主要验证，我们的系统将对20位健康志愿者进行测试。测试的结果将为我们提供初步数据关于雷达的准确性和基于深度的潮汐体积估计标准。 2。开发和验证一个框架，用于集成传感器的数据以检测呼吸系统沮丧。在这个具体目标中，我们将开发一个框架来使用呼吸速度，呼吸模式，以及来自雷达和深度摄像机的潮汐量信息，以确定呼吸抑郁是否具有发生。这涉及两步方法，我们提取呼吸特征以表征呼吸系统补充呼吸率和潮汐量的模式，然后使用机器学习模型来检测呼吸抑郁症的发生。为了帮助设计正确的模型，我们将使用雷达收集数据以及通过阿片类药物诱导的呼吸抑制的麻醉猪的深度成像系统。