RTM Vital Signs LLC — Department of Health and Human Services STTR Phase I: R41

Description

Abstract More thanAmericans died from drug overdose inand drug overdose is now the most common cause of death for people underyears old in the United StatesFurthermorethe number of overdose deaths is increasing with the rise of abuse of powerful synthetic opioidssuch as fentanylIn May ofNational Institutes of HealthNIHand National Institute on Drug AbuseNIDAdirectors DrsCollins and Volkow outlined how research may help reduce the death toll associated with the current opioid epidemicone of the current critical needs is the development of new overdose reversal interventionsincluding wearable technologies that can detect animpendingoverdose from physiological signals to signal for helpor trigger a coupled automated injection of naloxoneAutomated detection of overdose is essential because most opioid overdoses occur when individuals are alone and unobserved by family members or first respondersOpioids cause respiration to slow and become irregular due to mu opioid receptor mediated suppression of respiratory related regions of the brainstem and spinal cordImportantlythere are characteristic early changes in breathing pattern that indicate a progression towards significant hypoventilationbut there is currently no easy touse method or device to measure these patterns non invasivelyRecentlythere has been a renewed interest in respiratory monitoring using tracheal soundsTracheal sounds originate from the vibrations of the tracheal wall and surrounding soft tissues caused by gas pressure fluctuations in the tracheaThese sounds can be collected from a microphone placed over the trachea and analyzed to determine the real time respiratory rate and an estimate of respiratory flow and tidal volumeWe hypothesize that individual trends in tracheal sounds detected by a machinelearning algorithm will provide an early warning sign of the onset of hypoventilation as a result of opioid overdose in humansThe aims of this proposal are to develop a machine learning algorithm that detects impending hypoventilation due to an opioid overdose and to develop an initial design for a miniature wireless tracheal sound sensor Project Narrative RTM Vital SignsLLC is developing a non invasive Tracheal Sound Sensor for early detection of impending hypoventilation due to an opioid overdoseThe sensor will continuously monitor an individual s respiratory pattern to detect a significant change from baselinein which case it will contact a caregiver and or emergency personnel detailing the location and status of the person experiencing an opioid overdose or initiate a coupled naloxone injectionThis technology has the potential to prevent a significant number of deaths as a result of opioid overdose by allowing for the timely detection of hypoventilation and administration of naloxone