Overdoses from non-medical use of opioids can lead to hypoxemic/hypercarbic respiratory failure, cardiac arrest, and death when left untreated. Opioid toxicity is readily reversed with naloxone, a competitive antagonist that can restore respiration. However, there remains a critical need for technologies to administer naloxone in the event of unwitnessed overdose events. We report a closed-loop wearable injector system that measures respiration and apneic motion associated with an opioid overdose event using a pair of on-body accelerometers, and administers naloxone subcutaneously upon detection of an apnea. Our proof-of-concept system has been evaluated in two environments: (i) an approved supervised injection facility (SIF) where people self-inject opioids under medical supervision and (ii) a hospital environment where we simulate opioid-induced apneas in healthy participants. In the SIF (
Opioid overdose detection using smartphones
Early detection and rapid intervention can prevent death from opioid overdose. At high doses, opioids (particularly fentanyl) can cause rapid cessation of breathing (apnea), hypoxemic/hypercarbic respiratory failure, and death, the physiologic sequence by which people commonly succumb from unintentional opioid overdose. We present algorithms that run on smartphones and unobtrusively detect opioid overdose events and their precursors. Our proof-of- concept contactless system converts the phone into a short-range active sonar using frequency shifts to identify respiratory depression, apnea, and gross motor movements associated with acute opioid toxicity. We develop algorithms and perform testing in two environments: (i) an approved supervised injection facility (SIF), where people self-inject illicit opioids, and (ii) the operating room (OR), where we simulate rapid, opioid-induced overdose events using routine induction of general anesthesia. In the SIF ( n = 209), our system identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity and identified respiratory depression with 87% sensitivity and 89% specificity. These two key events commonly precede fatal opioid overdose. In the OR, our algorithm identified 19 of 20 simulated overdose events. Given the reliable reversibility of acute opioid toxicity, smartphone-enabled overdose detection coupled with the ability to alert naloxone-equipped friends and family or emergency medical services (EMS) could hold potential as a low-barrier, harm reduction intervention.
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- Award ID(s):
- 1812559
- NSF-PAR ID:
- 10095248
- Date Published:
- Journal Name:
- Science Translational Medicine
- Volume:
- 11
- Issue:
- 474
- ISSN:
- 1946-6234
- Page Range / eLocation ID:
- eaau8914
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract n = 25), our system identified breathing rate and post-injection respiratory depression accurately when compared to a respiratory belt. In the hospital, our algorithm identified simulated apneic events and successfully injected participants with 1.2 mg of naloxone. Naloxone delivery was verified by intravenous blood draw post-injection for all participants. A closed-loop naloxone injector system has the potential to complement existing evidence-based harm reduction strategies and, in the absence of bystanders, help make opioid toxicity events functionally witnessed and in turn more likely to be successfully resuscitated. -
Abstract Background Opioid-related overdose death is a public health epidemic in much of the USA, yet little is known about how people who use opioids (PWUO) experience overdose deaths in their social networks. We explore these experiences through a qualitative study of opioid-related overdose death bereavement among PWUO. Methods We recruited 30 adults who inject opioids from a syringe service program in the Midwestern USA and interviewed them using a semi-structured guide that addressed experiences of opioid use, opioid-related overdose, and overdose reversal via the medication naloxone. Interviews were transcribed verbatim and analyzed thematically. Findings Participants described overdose death as ever-present in their social worlds. Most (approximately 75%) reported at least one overdose death in their social network, and many came to consider death an inevitable end of opioid use. Participants described grief shaped by complex social relations and mourning that was interrupted due to involvement with social services and criminal legal systems. They also reported several ways that overdose deaths influenced their drug use, with some increasing their use and others adopting safer drug use practices. Despite the high prevalence of overdose deaths in their social networks, only one participant reported receiving grief support services. Discussion Findings underscore the need for interventions that not only maintain life, such as naloxone distribution, but also improve quality of life by attending to grief related to overdose death bereavement. We discuss policies and practices with the potential to address the unique psychological, social, and structural challenges of grief for this population.more » « less
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Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.more » « less
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null (Ed.)Abstract In 2016, more than 11 million Americans abused prescription opioids. The National Institute on Drug Abuse considers the opioid crisis a national addiction epidemic, as an increasing number of people are affected each year. Using the framework developed in mathematical modelling of infectious diseases, we create and analyse a compartmental opioid-abuse model consisting of a system of ordinary differential equations. Since $40\%$ of opioid overdoses are caused by prescription opioids, our model includes prescription compartments for the four most commonly prescribed opioids, as well as for the susceptible, addicted and recovered populations. While existing research has focused on drug abuse models in general and opioid models with one prescription compartment, no previous work has been done comparing the roles that the most commonly prescribed opioids have had on the crisis. By combining data from the Substance Abuse and Mental Health Services Administration (which tracked the proportion of people who used or misused one of the four individual opioids) with data from the Centers of Disease Control and Prevention (which counted the total number of prescriptions), we estimate prescription rates and probabilities of addiction for the four most commonly prescribed opioids. Additionally, we perform a sensitivity analysis and reallocate prescriptions to determine which opioid has the largest impact on the epidemic. Our results indicate that oxycodone prescriptions are both the most likely to lead to addiction and have the largest impact on the size of the epidemic, while hydrocodone prescriptions had the smallest impact.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/scitranslmed.aau8914
