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.
Public Health Relevance Statement: The US opioid crisis continues to have a catastrophic impact on human lives and the ongoing COVID-19 pandemic is compounding its effects. 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).
Project Terms: ages; Age; Anesthesia; Anesthesia procedures; Clothing; Complement; Complement Proteins; Involutional Depression; Involutional Melancholia; Pharmaceutical Preparations; Drugs; Medication; Pharmaceutic Preparations; drug/agent; Goals; Human; Modern Man; Medical Device; Movement; body movement; Persons; Patient Monitoring; Patients; Drug Prescriptions; Drug Prescribing; medication prescription; prescribed medication; Publishing; Radar; Respiration; respiratory mechanism; Sensitivity and Specificity; Signal Transduction; Cell Communication and Signaling; Cell Signaling; Intracellular Communication and Signaling; Signal Transduction Systems; Signaling; biological signal transduction; statistics; Family suidae; Pigs; Suidae; Swine; porcine; suid; Technology; Testing; Tidal Volume; respiratory airway volume; Work; Measures; sensor; Chest Wall; Thoracic Wall; Chest wall structure; Site; Area; Penetration; Phase; Respiratory Depression; depressed breathing; depression of breathing; Ventilatory Depression; Opiates; Opioid; Pattern; System; respiratory; Devices; Chest; Thorace; Thoracic; Thorax; Modeling; Dose; Data; Detection; research clinical testing; Clinical Evaluation; Clinical Testing; clinical test; Validation; validations; Monitor; Image; imaging; optic imaging; optical imaging; data integration; designing; design; Outcome; Consumption; healthy volunteer; noninvasive monitor; non-invasive monitor; prototype; multi-modality; multimodality; overdose fatalities; overdose death; licit opioid; opiate medication; opioid medication; prescribed opiate; prescribed opioid; prescription opiate; prescription opioid; image guidance; image guided; imaging system; opiate use disorder; opioid use disorder; opiate crisis; opioid crisis; opioid epidemic; Injections; patient with chronic pain; chronic pain patient; privacy preservation; COVID crisis; COVID epidemic; COVID pandemic; COVID-19 crisis; COVID-19 epidemic; COVID-19 global health crisis; COVID-19 global pandemic; COVID-19 health crisis; COVID-19 public health crisis; COVID19 crisis; COVID19 epidemic; COVID19 global health crisis; COVID19 global pandemic; COVID19 health crisis; COVID19 pandemic; COVID19 public health crisis; SARS-CoV-2 epidemic; SARS-CoV-2 global health crisis; SARS-CoV-2 global pandemic; SARS-CoV-2 pandemic; SARS-CoV2 epidemic; SARS-CoV2 pandemic; SARS-coronavirus-2 epidemic; SARS-coronavirus-2 pandemic; Severe Acute Respiratory Syndrome CoV 2 epidemic; Severe Acute Respiratory Syndrome CoV 2 pandemic; Severe acute respiratory syndrome coronavirus 2 epidemic; Severe acute respiratory syndrome coronavirus 2 pandemic; corona virus disease 2019 epidemic; corona virus disease 2019 pandemic; coronavirus disease 2019 crisis; coronavirus disease 2019 epidemic; coronavirus disease 2019 global health crisis; coronavirus disease 2019 global pandemic; coronavirus disease 2019 health crisis; coronavirus disease 2019 pandemic; coronavirus disease 2019 public health crisis; coronavirus disease crisis; coronavirus disease epidemic; coronavirus disease pandemic; coronavirus disease-19 global pandemic; coronavirus disease-19 pandemic; severe acute respiratory syndrome coronavirus 2 global health crisis; severe acute respiratory syndrome coronavirus 2 global pandemic; COVID-19 pandemic; remote monitoring; detection system; detection platform; homes; Home; machine learning framework; machine learning based framework; machine learning model; machine learning based model
