Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors.

Long-term and high-dose prescription opioid use places individuals at risk for opioid misuse, opioid use disorder (OUD), and overdose. Existing methods for monitoring opioid use and detecting misuse rely on self-reports, which are prone to reporting bias, and toxicology testing, which may be infeasible in outpatient settings. Although wearable technologies for monitoring day-to-day health metrics have gained significant traction in recent years due to their ease of use, flexibility, and advancements in sensor technology, their application within the opioid use space remains underexplored. In the current work, we demonstrate that oral opioid administrations can be detected using physiological signals collected from a wrist sensor. More importantly, we show that models informed by opioid pharmacokinetics increase reliability in predicting the timing of opioid administrations. Forty-two individuals who were prescribed opioids as a part of their medical treatment in-hospital and after discharge were enrolled. Participants wore a wrist sensor throughout the study, while opioid administrations were tracked using electronic medical records and self-reports. We collected 1,983 hours of sensor data containing 187 opioid administrations from the inpatient setting and 927 hours of sensor data containing 40 opioid administrations from the outpatient setting. We demonstrate that a self-supervised pre-trained model, capable of learning the canonical time series of plasma concentration of the drug derived from opioid pharmacokinetics, can reliably detect opioid administration in both settings. Our work suggests the potential of pharmacokinetic-informed, data-driven models to objectively detect opioid use in daily life.

Contactless Monitoring System Versus Gold Standard for Respiratory Rate Monitoring in Emergency Department Patients: Pilot Comparison Study.

BACKGROUND: Respiratory rate is a crucial indicator of disease severity yet is the most neglected vital sign. Subtle changes in respiratory rate may be the first sign of clinical deterioration in a variety of disease states. Current methods of respiratory rate monitoring are labor-intensive and sensitive to motion artifacts, which often leads to inaccurate readings or underreporting; therefore, new methods of respiratory monitoring are needed. The PulsON 440 (P440; TSDR Ultra Wideband Radios and Radars) radar module is a contactless sensor that uses an ultrawideband impulse radar to detect respiratory rate. It has previously demonstrated accuracy in a laboratory setting and may be a useful alternative for contactless respiratory monitoring in clinical settings; however, it has not yet been validated in a clinical setting. OBJECTIVE: The goal of this study was to (1) compare the P440 radar module to gold standard manual respiratory rate monitoring and standard of care telemetry respiratory monitoring through transthoracic impedance plethysmography and (2) compare the P440 radar to gold standard measurements of respiratory rate in subgroups based on sex and disease state. METHODS: This was a pilot study of adults aged 18 years or older being monitored in the emergency department. Participants were monitored with the P440 radar module for 2 hours and had gold standard (manual respiratory counting) and standard of care (telemetry) respiratory rates recorded at 15-minute intervals during that time. Respiratory rates between the P440, gold standard, and standard telemetry were compared using Bland-Altman plots and intraclass correlation coefficients. RESULTS: A total of 14 participants were enrolled in the study. The P440 and gold standard Bland-Altman analysis showed a bias of -0.76 (-11.16 to 9.65) and an intraclass correlation coefficient of 0.38 (95% CI 0.06-0.60). The P440 and gold standard had the best agreement at normal physiologic respiratory rates. There was no change in agreement between the P440 and the gold standard when grouped by admitting diagnosis or sex. CONCLUSIONS: Although the P440 did not have statistically significant agreement with gold standard respiratory rate monitoring, it did show a trend of increased agreement in the normal physiologic range, overestimating at low respiratory rates, and underestimating at high respiratory rates. This trend is important for adjusting future models to be able to accurately detect respiratory rates. Once validated, the contactless respiratory monitor provides a unique solution for monitoring patients in a variety of settings.

A mobile addiction service for community-based overdose prevention.

Mainstays of opioid overdose prevention include medications for opioid use disorder (e.g., methadone or buprenorphine) and naloxone distribution. Inadequate access to buprenorphine limits its uptake, especially in communities of color, and people with opioid use disorders encounter multiple barriers to obtaining necessary medications including insurance, transportation, and consistent availability of telephones. UMass Memorial Medical Center and our community partners sought to alleviate these barriers to treatment through the deployment of a mobile addiction service, called the Road to Care. Using this approach, multidisciplinary and interprofessional providers deliver holistic addiction care by centering our patients' needs with respect to scheduling, location, and convenience. This program also extends access to buprenorphine and naloxone among people experiencing homelessness. Additional systemic and individualized barriers encountered are identified, as well as potential solutions for future mobile addiction service utilization. Over a two-year period, we have cared for 1,121 individuals who have accessed our mobile addiction service in over 4,567 encounters. We prescribed buprenorphine/naloxone (Suboxone®) to 330 individuals (29.4% of all patients). We have distributed nearly 250 naloxone kits directly on-site or and more than 300 kits via prescriptions to local pharmacies. To date, 74 naloxone rescue attempts have been reported back to us. We have demonstrated that a community-based mobile addiction service, anchored within a major medical center, can provide high-volume and high-quality overdose prevention services that facilitate engagement with additional treatment. Our experience is described as a case study below.

Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Experiences with Medications for Addiction Treatment Among Emergency Department Patients with Opioid Use Disorder.

INTRODUCTION: Medications for addiction treatment (MAT) are the evidence-based standard of care for treatment of opioid use disorder (OUD), but stigma continues to surround their use. We conducted an exploratory study to characterize perceptions of different types of MAT among people who use drugs. METHODS: We conducted this qualitative study in adults with a history of non-medical opioid use who presented to an emergency department for complications of OUD. A semi-structured interview that explored knowledge, perceptions, and attitudes toward MAT was administered, and applied thematic analysis conducted. RESULTS: We enrolled 20 adults. All participants had prior experience with MAT. Among participants indicating a preferred treatment modality, buprenorphine was the commonly favored agent. Previous experience with prolonged withdrawal symptoms upon MAT discontinuation and the perception of "trading one drug for another" were common reasons for reluctance to engage in agonist or partial-agonist therapy. While some participants preferred treatment with naltrexone, others were unwilling to initiate antagonist therapy due to fear of precipitated withdrawal. Most participants strongly considered the aversive nature of MAT discontinuation as a barrier to initiating treatment. Participants overall viewed MAT positively, but many had strong preferences for a particular agent. CONCLUSION: The anticipation of withdrawal symptoms during initiation and cessation of treatment affected willingness to engage in a specific therapy. Future educational materials for people who use drugs may focus on comparisons of respective benefits and drawbacks of agonists, partial agonists, and antagonists. Emergency clinicians must be prepared to answer questions about MAT discontinuation to effectively engage patients with OUD.

Perceptions on wearable sensor-based interventions for monitoring of opioid therapy: A qualitative study.

Prescription opioid use is a risk factor for the development of opioid use disorder. Digital solutions, including wearable sensors, represent a promising opportunity for health monitoring, risk stratification and harm reduction in this treatment space. However, data on their usability and acceptability in individuals using opioids is limited. To address this gap, factors that impact usability and acceptability of wearable sensor-based opioid detection were qualitatively studied in participants enrolled in a wearable sensor-based opioid monitoring research study. At the conclusion of the monitoring period, participants were invited to take part in semi-structured interviews developed based on the technology acceptance model. Thematic analysis was conducted first using deductive, then inductive coding strategies. Forty-four participants completed the interview; approximately half were female. Major emergent themes include sensor usability, change in behavior and thought process related to sensor use, perceived usefulness in sensor-based monitoring, and willingness to have opioid use patterns monitored. Overall acceptance for sensor-based monitoring was high. Aesthetics, simplicity, and seamless functioning were all reported as key to usability. Perceived behavior changes related to monitoring were infrequent while perceived usefulness in monitoring was frequently projected onto others, requiring careful consideration regarding intervention development and targeting. Specifically, care must be taken to avoid stigma associated with opioid use and implied misuse. The design of sensor systems targeted for opioid use must also consider the physical, social, and cognitive alterations inherent in the respective disease processes compared to routine daily life.

Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use.

Opioid use disorder is one of the most pressing public health problems of our time. Mobile health tools, including wearable sensors, have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with health or pathology. The current manuscript describes a longitudinal, observational study of adult patients receiving opioid analgesics for acute painful conditions. Participants in the study are monitored with a wrist-worn E4 sensor, during which time physiologic parameters (heart rate/variability, electrodermal activity, skin temperature, and accelerometry) are collected continuously. Opioid use events are recorded via electronic medical record and self-report. Three-hundred thirty-nine discreet dose opioid events from 36 participant are analyzed among 2070 h of sensor data. Fifty-one features are extracted from the data and initially compared pre- and post-opioid administration, and subsequently are used to generate machine learning models. Model performance is compared based on individual and treatment characteristics. The best performing machine learning model to detect opioid administration is a Channel-Temporal Attention-Temporal Convolutional Network (CTA-TCN) model using raw data from the wearable sensor. History of intravenous drug use is associated with better model performance, while middle age, and co-administration of non-narcotic analgesia or sedative drugs are associated with worse model performance. These characteristics may be candidate input features for future opioid detection model iterations. Once mature, this technology could provide clinicians with actionable data on opioid use patterns in real-world settings, and predictive analytics for early identification of opioid use disorder risk.

Sentanyl: a comparison of blood fentanyl concentrations and naloxone dosing after non-fatal overdose.

INTRODUCTION: Non-pharmaceutical fentanyl and its analogs have driven striking increases in opioid-associated overdose deaths. These highly potent opioids can be found at low concentrations in biological specimens. Little is known regarding the concentrations of these substances among survivors of non-fatal overdoses. In a locale where fentanyl is responsible for the majority of non-fatal opioid overdoses, we compared the concentration of fentanyl in blood to naloxone dosing in the presence and absence of a concurrent sedative-hypnotic exposure. METHODS: In this pilot study, we enrolled adult patients presenting to the emergency department (ED) who: (1) arrived after an overdose requiring naloxone for the reversal of respiratory depression; and (2) who required venipuncture or intravenous access as part of their clinical care. Blood specimens (n = 20) underwent comprehensive toxicology testing, including the quantitation of fentanyl, fentanyl analogs, and naloxone, as well as the detection of common sedative-hypnotics and a wide range of other illicit and pharmaceutical substances. We then compared fentanyl concentrations to naloxone dosing in participants with and without a concomitant sedative-hypnotic exposure. RESULTS: Nineteen of twenty participants (95%) were exposed to fentanyl prior to their overdose; the remaining participant tested positive for heroin metabolites. No participants reported pharmaceutical fentanyl use. Fentanyl analogs - acetylfentanyl or carfentanil - were present in three specimens. In 11 cases, fentanyl and its metabolites were the only opioids identified. Among the fentanyl-exposed, blood concentrations ranged from <0.1-19 ng/mL with a mean of 6.2 ng/mL and a median of 3.6 ng/mL. There was no relationship between fentanyl concentration and naloxone dose administered for reversal. We detected sedative-hypnotics (including benzodiazepines, muscle relaxants, and antidepressants) in nine participants. Among the sedative-hypnotic exposed, fentanyl concentrations were lower, but naloxone dosing was similar to those without a concomitant exposure. CONCLUSIONS: In this study, we found that: 1) fentanyl was present in the blood of nearly all participants; 2) fentanyl concentrations were lower among study participants with concomitant sedative-hypnotic exposure; and 3) the dose of naloxone administered for overdose reversal was not associated with the measured fentanyl concentration in blood specimens. Our results underscore the role that tolerance and concomitant drug exposure play in the precipitation and resuscitation of management of opioid overdose.

Cannabis Product Ingestions in Pediatric Patients: Ranges of Exposure, Effects, and Outcomes.

INTRODUCTION: Pediatric exposures to cannabis edibles have been associated with serious adverse effects, such as respiratory depression. Yet, their incidence and relationship to exposure characteristics are not well defined. We attempt to describe the temporal, demographic, and clinical characteristics of pediatric patients with edible cannabis exposures and examine the relationship between these characteristics and two clinical outcomes: need for respiratory support and hospital admission. METHODS: A retrospective chart review was conducted at a single, tertiary care academic medical center covering a 28-month period. Inclusion criteria were: evaluation in the ED, age <18 years at the time of presentation, and physician documented exposure to edible cannabis. Exclusion criteria were: known or suspected co-ingestion of other substances. RESULTS: Thirty-two cases of edible cannabis ingestions were identified. Age <10 years was associated with bradypnea, hypertension, hospital admission, and respiratory support. Use of respiratory support was significantly associated with the presence of lethargy, bradypnea, hypercarbia, seizure, and hypertension. There was a five-fold increase in the number of pediatric edible cannabis exposures after recreational cannabis dispensaries opened in Massachusetts. Five patients (16%) required respiratory support and eleven (34%) required hospital admission. CONCLUSIONS: There was a low incidence of need for respiratory support in our population, but hospital admission was more common. Severe symptoms (including lethargy and respiratory depression), need for respiratory support and hospital admission were more frequent in younger children. Exposures occurred with increasing frequency over time. Larger studies are needed to explore the relationship between THC dosage, age, and incidence of adverse outcomes.

Nobody Wants to Be Narcan'd: A Pilot Qualitative Analysis of Drug Users' Perspectives on Naloxone.

INTRODUCTION: Bystander naloxone distribution is an important component of public health initiatives to decrease opioid-related deaths. While there is evidence supporting naloxone distribution programs, the effects of increasing naloxone availability on the behavior of people who use drugs have not been adequately delineated. In this study we sought to 1) evaluate whether individuals' drug use patterns have changed due to naloxone availability; and 2) explore individuals' knowledge of, access to, experiences with, and perceptions of naloxone. METHODS: We conducted a pilot study of adults presenting to the emergency department whose medical history included non-medical opioid use. Semi-structured interviews were conducted with participants and thematic analysis was used to code and analyze interview transcripts. RESULTS: Ten participants completed the study. All were aware of naloxone by brand name (Narcan) and had been trained in its use, and all but one had either currently or previously possessed a kit. Barriers to naloxone administration included fear of legal repercussions, not having it available, and a desire to avoid interrupting another user's "high." Of the eight participants who reported being revived with naloxone at least once during their lifetime, all described experiencing a noxious physical response and expressed a desire to avoid receiving it again. Furthermore, participants did not report increasing their use of opioids when naloxone was available. CONCLUSIONS: Participants were accepting of and knowledgeable about naloxone, and were willing to administer naloxone to save a life. Participants tended to use opioids more cautiously when naloxone was present due to fears of experiencing precipitated withdrawal. This study provides preliminary evidence countering the unsubstantiated narrative that increased naloxone availability begets more high-risk opioid use and further supports increasing naloxone access.

OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.

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 R 2 coefficient of 0.85.

A Case of Unintentional Opioid (U-47700) Overdose in a Young Adult After Counterfeit Xanax Use.

ABSTRACT: We report the case of a young adult who became unresponsive after insufflating what he believed to be "crushed Xanax." Naloxone was administered, reversing his altered mental status and respiratory depression. Clinicians suspected opioid toxicity; however, the patient adamantly denied opioid use. Because of unclear etiology of his symptoms, blood and urine specimens were obtained. A urine specimen was split and then submitted for a clinical comprehensive drug screen using gas chromatography-mass spectrometry. The blood specimen and the remaining urine specimen were sent to a reference laboratory for analysis using liquid chromatography quadrupole time-of-flight mass spectrometry and liquid chromatography tandem mass spectrometry. The standard, clinical gas chromatography-mass spectrometry urine drug testing procedure only detected caffeine; however, analysis by liquid chromatography quadrupole time-of-flight mass spectrometry and liquid chromatography tandem mass spectrometry confirmed the presence of U-47700 (a high-potency clandestine opioid) and its metabolites in the urine and blood. These findings implicate U-47700 as the agent responsible for the patient's signs of opioid toxicity. In this case, a young adult intending to use alprazolam encountered U-47700 with life-threatening effect. Clinicians must remain vigilant for symptoms consistent with opioid overdose, especially with increasing prevalence of counterfeit drugs containing clandestine opioids. Clinicians must also consider obtaining specimens for appropriate analytical testing to improve surveillance and facilitate public health interventions.

Understanding Naloxone Uptake from an Emergency Department Distribution Program Using a Low-Energy Bluetooth Real-time Location System.

INTRODUCTION: Emergency department (ED)-based naloxone distribution programs are a widespread harm reduction strategy. However, data describing the community penetrance of naloxone distributed from these programs are lacking. This study gauges acceptance of naloxone use and monitoring technology among people who use drugs (PWUD), and explores the use of real-time location systems (RTLS) in monitoring naloxone movements. METHODS: A prospective observational study was conducted on a convenience sample of individuals (N = 30) presenting to a tertiary-care academic medical center ED for an opioid-related complaint. A naloxone kit equipped with a low-energy Bluetooth (BLE) tracking system was employed to detect movement of naloxone off the hospital campus as a proxy for community penetrance, followed by a qualitative interview to gauge participant acceptance of naloxone use and monitoring technology. RESULTS: Detection of BLE signals verified transit of 24 distributed naloxone kits off our hospital campus. Three participants whose BLE signals were not captured reported taking their kits with them following discharge, suggesting technological errors occurred; another three participants were lost to follow-up. Qualitative interviews demonstrated that participants accepted ED-based naloxone distribution programs and passive tracking technologies, but revealed concerns regarding hypothetical continuous monitoring systems and problematic interactions with first responders and law enforcement personnel. CONCLUSIONS: Based on acquired BLE signals, 80% of dispensed naloxone kits left the hospital campus. Use of RTLS to passively geolocate naloxone rescue kits is feasible, but detection can be adversely affected by technological errors. PWUD are amenable to transient monitoring technologies but identified barriers to implementation.

Federal Funding in Emergency Medicine: Demographics and Perspectives of Awardees.

INTRODUCTION: Emergency physicians face multiple challenges to obtaining federal funding. The objective of this investigation was to describe the demographics of federally-funded emergency physicians and identify key challenges in obtaining funding. METHODS: We conducted a retrospective database search of the National Institutes of Health (NIH) Research Portfolio Online Reporting Tool (NIH RePORTER) to collect data regarding the distribution and characteristics of federally-funded grants awarded to emergency medicine (EM) principal investigators between 2010-2017. An electronic survey was then administered to the identified investigators to obtain additional demographic data, and information regarding their career paths, research environment, and perceived barriers to obtaining federal funding. RESULTS: We identified 219, corresponding to 51 unique, mentored career development awardees and 105 independent investigators. Sixty-two percent of investigators responded to the electronic survey. Awardees were predominantly White males, although a larger portion of the mentored awardee group was female. Greater than half of respondents reported their mentor to be outside of the field of EM. The most common awarding institution was the National Heart Lung and Blood Institute. Respondents identified barriers in finding adequate mentorship, time to gather preliminary data, and the quality of administrative support. CONCLUSION: The last five years have showed a trend toward increasing grants awarded to EM investigators; however, we identified several barriers to funding. Initiatives geared toward support and mentorship of junior faculty, particularly to females, minorities, and those in less heavily funded areas of the country are warranted.

Respiratory Rate Monitoring in Clinical Environments with a Contactless Ultra-Wideband Impulse Radar-based Sensor System.

Respiratory rate is an extremely important but poorly monitored vital sign for medical conditions. Current modalities for respiratory monitoring are suboptimal. This paper presents a proof of concept of a new algorithm using a contactless ultra-wideband (UWB) impulse radar-based sensor to detect respiratory rate in both a laboratory setting and in a two-subject case study in the Emergency Department. This novel approach has shown correlation with manual respiratory rate in the laboratory setting and shows promise in Emergency Department subjects. In order to improve respiratory rate monitoring, the UWB technology is also able to localize subject movement throughout the room. This technology has potential for utilization both in and out of the hospital environments to improve monitoring and to prevent morbidity and mortality from a variety of medical conditions associated with changes in respiratory rate.

A Pilot Study of a Telemedicine-based Substance Use Disorder Evaluation to Enhance Access to Treatment Following Near-Fatal Opioid Overdose.

The opioid epidemic is a growing public health emergency in the United States, with deaths from opioid overdose having increased five-fold since 1999. Emergency departments (EDs) are the primary sites of medical care after near-fatal opioid overdose but are poorly equipped to provide adequate substance use treatment planning prior to discharge. In many underserved locales, limited access to clinicians trained in addiction medicine and behavioral health exacerbates this disparity. In an effort to improve post-overdose care in the ED, we developed a telemedicine protocol to facilitate timely access to substance use disorder evaluations. In this paper, we describe the conception and refinement of the telemedicine program, our experience with the first 20 participants, and potential implications of the platform on health disparities for individuals with opioid use disorder.

Low-energy Bluetooth for detecting real-world penetrance of bystander naloxone kits: a pilot study.

Opioid overdose is a growing public health emergency in the United States. The antidote naloxone must be administered rapidly after opioid overdose to prevent death. Bystander or "take-home" naloxone programs distribute naloxone to opioid users and other community members to increase naloxone availability at the time of overdose. However, data describing the natural history of take-home naloxone in the hands of at-risk individuals is lacking. To understand patterns of naloxone uptake in at-risk users, we developed a smart naloxone kit that uses low-energy Bluetooth (BLE) to unobtrusively detect the transit of naloxone through a hospital campus. In this paper, we describe development of the smart naloxone kit and results from the first 10 participants in our pilot study.

A Novel Oral Fluid Assay (LC-QTOF-MS) for the Detection of Fentanyl and Clandestine Opioids in Oral Fluid After Reported Heroin Overdose.

INTRODUCTION: The adulteration of heroin with non-pharmaceutical fentanyl and other high-potency opioids is one of the factors contributing to striking increases in overdose deaths. To fully understand the magnitude of this problem, accurate detection methods for fentanyl and other novel opioid adulterant exposures are urgently required. The objective of this work was to compare the detection of fentanyl in oral fluid and urine specimens using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in a population of heroin users presenting to the Emergency Department after overdose. METHODS: This was a prospective observational study of adult Emergency Department patients who presented after a reported heroin overdose requiring naloxone administration. Participants provided paired oral fluid and urine specimens, which were prepared, extracted, and analyzed using a dual LC-QTOF-MS workflow for the identification of traditional and emerging drugs of abuse. Analytical instrumentation included SCIEX TripleTOF® 5600+ and Waters Xevo® G2-S QTOF systems. RESULTS: Thirty participants (N = 30) were enrolled during the study period. Twenty-nine participants had fentanyl detected in their urine, while 27 had fentanyl identified in their oral fluid (overall agreement 93.3%, positive percent agreement 93.1%). Cohen's Kappa (k) was calculated and demonstrated moderately, significant agreement (k = 0.47; p value 0.002) in fentanyl detection between oral fluid and urine using this LC-QTOF-MS methodology. Additional novel opioids and metabolites, including norfentanyl, acetylfentanyl, and U-47700, were detected during this study. CONCLUSION: In this study of individuals presenting to the ED after reported heroin overdose, a strikingly high proportion had a detectable fentanyl exposure. Using LC-QTOF-MS, the agreement between paired oral fluid and urine testing for fentanyl detection indicates a role for oral fluid testing in surveillance for nonpharmaceutical fentanyl. Additionally, the use of LC-QTOF-MS allowed for the detection of other clandestine opioids (acetylfentanyl and U-47700) in oral fluid.