Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

BACKGROUND: Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups. METHODS: Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data. RESULTS: Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%. CONCLUSIONS: Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure.

Detection of ethanol, cannabinoids, benzodiazepines, and opioids in older adults evaluated for serious injuries from falls.

BACKGROUND: In 2020, there were 36.7 million reported falls among older adults (65+) in the United States. Ethanol and other sedating substances may increase fall risk among older adults due to their effect on cognitive and physical function. We estimate the prevalence of these substances in blood specimens of older adults presenting with a fall injury at selected trauma centers. METHODS: The initial study collected blood specimens from May 2020 through July 2021 from adults undergoing a trauma team evaluation at selected United States Level 1 trauma centers. We limited our study to older adults evaluated after a fall (n = 1,365) and selected a random sample (n = 300) based on age, sex, and trauma-center quotas. Medical health records and blood specimens obtained at trauma center presentation were analyzed. We estimated the prevalence of ethanol, benzodiazepines, cannabinoids, and opioids in the blood specimens. Two-sample tests of binomial proportions and Chi-square two-tailed tests were used to compare prevalence estimates of substances by demographic characteristics. RESULTS: At least one substance was detected among 31.3% of samples analyzed. Prevalences of specific substances detected were 9.3% (95% CI: 6.0-12.6%) for benzodiazepines, 4.3% (95% CI: 2.0-6.7%) for cannabinoids, 8.0% (95% CI: 5.2-11.7%) for ethanol, and 15.0% (95% CI: 10.9-19.1%) for opioids. There were 18 deaths (6%; 95% CI: 3.6-9.3%). One-third of decedents had at least one substance detected in their blood. DISCUSSION: Opioids were the most frequently detected substance, followed by benzodiazepines, ethanol, and cannabinoids. Substance use prevalence was not uniform across demographics, with differences observed by sex and age. CONCLUSIONS: This study provides insight into the frequency of the presence of substances that may contribute to fall risk and serious injury among older adults. Screening older adults for substances that impair cognitive and physical function can enhance clinical fall prevention efforts.

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.

Opioid Overdose Recognition: A Survey of Perceived Preparedness and Desire for Curricular Integration Among Current US Medical Students.

OBJECTIVES: Opioid overdose deaths remain a major health issue in the United States (US). As future physicians, medical students must receive comprehensive training to recognize and manage opioid overdoses. This study aimed to highlight training gaps at the medical student level and understand students' attitudes toward patients with opioid use disorder (OUD). METHODS: We assessed baseline knowledge of and attitudes toward the management of opioid overdoses and naloxone administration among medical students in the US. Two validated survey tools (Opioid Overdose Knowledge Scale and Opioid Overdose Attitude Scale) were administered to medical students training at accredited institutions along with supplemental questions measuring knowledge and attitudes towards opioid overdose management, naloxone administration, and prior training. RESULTS: The final sample had N = 73 participants from US medical schools with a mean age of 25.3 (range of 22-37): 72.6% of respondents were female. Although most respondents reported personal/professional experience with OUD before medical school, they expressed interest in additional training. Knowledge surrounding opioid overdoses increased insignificantly over the 4 years of medical school. However, there was a significant increase in both perceived competence in overdose recognition/management and in concerns about intervening from the first to fourth year of medical school. Female respondents had significantly lower perceived competence and readiness to intervene sub-scores than male counterparts; however, there was no significant difference in overall attitude and knowledge scores when stratified by sex. Incorporating opioid overdose prevention training (OOPT) into early medical education was favorable among respondents, who expressed an overwhelming interest in learning and supporting patients with OUD. CONCLUSIONS: Given the ongoing opioid crisis, medical students are ideally placed to identify and manage opioid overdoses. Medical students are ready to receive this training, thus strengthening the argument for OOPT integration into early medical student curricula.

Contralateral Prophylactic Mastectomy Decision-Making: The Partners' Perspective.

BACKGROUND: The rate of contralateral prophylactic mastectomy (CPM) continues to rise despite no improvement in survival, an increased risk of surgical complications, and negative effects on quality of life. This study explored the experiences of the partners of women who undergo CPM. METHODS: This study was part of an investigation into the factors motivating women with early-stage unilateral breast cancer and low genetic risk to opt for contralateral prophylactic mastectomy (CPM). Participating women were asked for permission to invite their partners to take part in interviews. In-depth interviews with partners were conducted using a semi-structured topic guide. A thematic analysis of the data was performed RESULTS: Of 35 partners, all men, 15 agreed to be interviewed. Most perceived their role to be strong and logical. Some hoped their wives would choose a bilateral mastectomy. All felt strongly that the final decision was up to their partners. The partners often framed the decision for CPM as one of life or death. Thus, any aesthetic effects were unimportant by comparison. The male partners had difficulty grasping the physical and emotional changes inherent in mastectomy, which made communicating about sexuality and intimacy very challenging for the couples. In the early recovery period, some noted the stress of managing home life. CONCLUSIONS: The experiences of the male partners provide insight into how couples navigate complex treatment decision-making, both together and separately. There may be a benefit to including partners in pre- and post-surgical counseling to mitigate miscommunication regarding the expected oncologic and emotional outcomes related to CPM.

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.

Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder.

Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.

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.

Cerebellar control of thalamocortical circuits for cognitive function: A review of pathways and a proposed mechanism.

There is general agreement that cerebrocerebellar interactions via cerebellothalamocortical pathways are essential for a cerebellar cognitive and motor functions. Cerebellothalamic projections were long believed target mainly the ventral lateral (VL) and part of the ventral anterior (VA) nuclei, which project to cortical motor and premotor areas. Here we review new insights from detailed tracing studies, which show that projections from the cerebellum to the thalamus are widespread and reach almost every thalamic subnucleus, including nuclei involved in cognitive functions. These new insights into cerebellothalamic pathways beyond the motor thalamus are consistent with the increasing evidence of cerebellar cognitive function. However, the function of cerebellothalamic pathways and how they are involved in the various motor and cognitive functions of the cerebellum is still unknown. We briefly review literature on the role of the thalamus in coordinating the coherence of neuronal oscillations in the neocortex. The coherence of oscillations, which measures the stability of the phase relationship between two oscillations of the same frequency, is considered an indicator of increased functional connectivity between two structures showing coherent oscillations. Through thalamocortical interactions coherence patterns dynamically create and dissolve functional cerebral cortical networks in a task dependent manner. Finally, we review evidence for an involvement of the cerebellum in coordinating coherence of oscillations between cerebral cortical structures. We conclude that cerebellothalamic pathways provide the necessary anatomical substrate for a proposed role of the cerebellum in coordinating neuronal communication between cerebral cortical areas by coordinating the coherence of oscillations.

Targeted Modulation of Chicken Genes In Vitro Using CRISPRa and CRISPRi Toolkit.

Engineering of clustered regularly interspaced short palindromic repeats (CRISPR) and the CRISPR-associated protein 9 (Cas9) system has enabled versatile applications of CRISPR beyond targeted DNA cleavage. Combination of nuclease-deactivated Cas9 (dCas9) and transcriptional effector domains allows activation (CRISPRa) or repression (CRISPRi) of target loci. To demonstrate the effectiveness of the CRISPR-mediated transcriptional regulation in chickens, three CRISPRa (VP64, VPR, and p300) and three CRISPRi (dCas9, dCas9-KRAB, and dCas9-KRAB-MeCP2) systems were tested in chicken DF-1 cells. By introducing guide RNAs (gRNAs) targeting near the transcription start site (TSS) of each gene in CRISPRa and CRISPRi effector domain-expressing chicken DF-1 cell lines, significant gene upregulation was induced in dCas9-VPR and dCas9-VP64 cells, while significant downregulation was observed with dCas9 and dCas9-KRAB. We further investigated the effect of gRNA positions across TSS and discovered that the location of gRNA is an important factor for targeted gene regulation. RNA sequencing analysis of IRF7 CRISPRa and CRISPRi- DF-1 cells revealed the specificity of CRISPRa and CRISPRi-based targeted transcriptional regulation with minimal off-target effects. These findings suggest that the CRISPRa and CRISPRi toolkits are an effective and adaptable platform for studying the chicken genome by targeted transcriptional modulation.

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.

Evidence for Thalamic Responsive Neurostimulation in Treatment of Adult and Pediatric Epilepsy.

Responsive neurostimulation (RNS) has well-established efficacy in patients with identifiable seizure foci. Emerging evidence suggests the feasibility of expanding this treatment to patients with nonfocal or multifocal epileptic profiles with thalamic targeting. Our institution performed two successful implantations of thalamic RNS (tRNS) targeting the centromedian nucleus of the thalamus (CMT), and 1-year postoperative outcomes are provided. Additionally, a literature review of all reported tRNS was conducted. Publications were excluded if they did not include demographic data and/or epilepsy outcomes at follow-up. In the literature, 19 adult and 3 pediatric cases were identified. These cases were analyzed for outcome, indications, previous operations, and surgical practice variations. Both of our patients had failed multiple previous pharmacological and neurosurgical interventions for epilepsy. Case #1 underwent tRNS with bilateral CMT stimulation. Case #2 underwent tRNS with simultaneous right CMT and right insular stimulation, although an additional lead was placed in the left CMT and left capped for potential future use. Each has achieved ≥90% reduction in seizure burden and approach seizure freedom. 71% of patients in the literature review had multifocal, bilateral, or cryptogenic seizure onset. Three patients were implanted for Lennox Gastaut (2 of 3 are pediatric). 16 patients underwent an average of 1.6 failed procedures prior to successful tRNS implantation. Taken together, the 21 adult patients reviewed have experienced an average seizure reduction of 77% at the latest follow-up. 95% of the adult patients reported in the literature experienced >50% reduction in seizure activity following tRNS and 52% experienced ≥90% reduction in seizure burden following tRNS. Pediatric patients have experienced 70-100% improvement.

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.

Angiotensin Receptor Neprilysin Inhibition and Associated Outcomes by Race and Ethnicity in Patients With Heart Failure With Reduced Ejection Fraction: Data From CHAMP-HF.

Background There are limited data on the use of angiotensin receptor neprilysin inhibitors (ARNIs) in minority populations with heart failure (HF) with reduced ejection fraction. We used data from the CHAMP-HF (Change the Management of Patients With Heart Failure) registry to evaluate ARNI initiation and associated changes in health status and clinical outcomes across different races and ethnicities. Methods and Results CHAMP-HF was a prospective, observational registry of US outpatients with chronic HF with reduced ejection fraction. We compared patients starting ARNI with patients not starting ARNI using a propensity-matched analysis. Patients were grouped as Hispanic, non-Hispanic Black, non-Hispanic White, or non-Hispanic other individuals, where "non-Hispanic other" consists of all patients who did not identify as Hispanic, Black, or White. Health status was assessed using the 12-item Kansas City Cardiomyopathy Questionnaire. Outcomes were analyzed with multivariable models that included race and ethnicity, ARNI initiation, and an interaction term between race and ethnicity and ARNI initiation. Cox proportional hazards models were used for death/HF hospitalization, and multiple regression was used for change in Kansas City Cardiomyopathy Questionnaire score. The analysis included 1516 patients, with 758 patients in each group (ARNI and no ARNI). Changes in Kansas City Cardiomyopathy Questionnaire score after ARNI initiation were similar among all race and ethnicity groups (mean [SD], non-Hispanic White individuals, 3.5 [19.0]; non-Hispanic Black individuals, 2.0 [17.0]; non-Hispanic other individuals, 5.5 [20.3]; and Hispanic individuals, 3.2 [20.1]), with no statistically significant interaction between race and ethnicity and ARNI initiation (P=0.21). There was similarly no statistically significant interaction between race and ethnicity and ARNI initiation for HF hospitalization (P=0.82) or all-cause mortality (P=0.92). Conclusions In a large registry of outpatients with HF with reduced ejection fraction, the association between ARNI initiation and outcomes did not differ by race and ethnicity. These data support the use of ARNI therapy for chronic HF with reduced ejection fraction irrespective of race and ethnicity.

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.

Deficiencies in the DNA Binding Protein ARID3a Alter Chromatin Structures Important for Early Human Erythropoiesis.

ARID3a is a DNA-binding protein important for normal hematopoiesis in mice and for in vitro lymphocyte development in human cultures. ARID3a knockout mice die in utero with defects in both early hematopoietic stem cell populations and erythropoiesis. Recent transcriptome analyses in human erythropoietic systems revealed increases in ARID3a transcripts implicating potential roles for ARID3a in human erythrocyte development. However, ARID3a transcript levels do not faithfully reflect protein levels in many cells, and the functions and requirements for ARID3a protein in those systems have not been explored. We used the erythroleukemic cell line K562 as a model to elucidate functions of ARID3a protein in early human erythropoiesis. ARID3a knockdown of hemin-stimulated K562 cells resulted in lack of fetal globin production and modifications in gene expression. Temporal RNA sequencing data link ARID3a expression with the important erythroid regulators Gata1, Gata2, and Klf1 Ablation of ARID3a using CRISPR-Cas9 further demonstrated it is required to maintain chromatin structures associated with erythropoietic differentiation potential. These data demonstrate that the ARID3a protein is required for early erythropoietic events and provide evidence for the requirement of ARID3a functions for proper maintenance of appropriate chromatin structures.