Willingness to Use a Wearable Device Capable of Detecting and Reversing Overdose Among People Who Use Opioids in Philadelphia

Background: The incidence of opioid-related overdose deaths has been rising for 30 years and has been further exacerbated amidst the COVID-19 pandemic. Naloxone can reverse opioid overdose, lower death rates, and enable a transition to medication for opioid use disorder. Though current formulations for community use of naloxone have been shown to be safe and effective public health interventions, they rely on bystander presence. We sought to understand the preferences and minimum necessary conditions for wearing a device capable of sensing and reversing opioid overdose among people who regularly use opioids. Methods: We conducted a combined cross-sectional survey and semi-structured interview at a respite center, shelter, and syringe exchange drop-in program in Philadelphia, Pennsylvania, USA during the COVID-19 pandemic in August and September 2020. The primary aim was to explore the proportion of participants who would use a wearable device to detect and reverse overdose. Preferences regarding designs and functionalities were collected via a questionnaire with items having Likert-based response options and a semi-structured interview intended to elicit feedback on prototype designs. Independent variables included demographics, opioid use habits, and previous experience with overdose. Results: A total of 97 adults with an opioid-use history of at least 3 months were interviewed. A majority of survey participants (76%) reported a willingness to use a device capable of detecting an overdose and automatically administering a reversal agent upon initial survey. When reflecting on the prototype, most respondents (75.5%) reported that they would wear the device always or most of the time. Respondents indicated discreetness and comfort as important factors that increased their chance of uptake. Respondents suggested that people experiencing homelessness and those with low tolerance for opioids would be in greatest need of the device. Conclusions: The majority of people sampled with a history of opioid use in an urban setting were interested in having access to a device capable of detecting and reversing an opioid overdose. Participants emphasized privacy and comfort as the most important factors influencing their willingness to use such a device. Trial Registration: NCT0453059

Deep Learning for Classification of Bone Lesions on Routine MRI

Background: Radiologists have difficulty distinguishing benign from malignant bone lesions because these lesions may have similar imaging appearances. The purpose of this study was to develop a deep learning algorithm that can differentiate benign and malignant bone lesions using routine magnetic resonance imaging (MRI) and patient demographics. Methods: 1,060 histologically confirmed bone lesions with T1- and T2-weighted pre-operative MRI were retrospectively identified and included, with lesions from 4 institutions used for model development and internal validation, and data from a fifth institution used for external validation. Image-based models were generated using the EfficientNet-B0 architecture and a logistic regression model was trained using patient age, sex, and lesion location. A voting ensemble was created as the final model. The performance of the model was compared to classification performance by radiology experts. Findings: The cohort had a mean age of 30±23 years and was 58.3% male, with 582 benign lesions and 478 malignant. Compared to a contrived expert committee result, the ensemble deep learning model achieved (ensemble vs. experts): similar accuracy (0·76 vs. 0·73, p=0·7), sensitivity (0·79 vs. 0·81, p=1·0) and specificity (0·75 vs. 0·66, p=0·48), with a ROC AUC of 0·82. On external testing, the model achieved ROC AUC of 0·79. Interpretation: Deep learning can be used to distinguish benign and malignant bone lesions on par with experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers from community clinics and limit unnecessary biopsies. Funding: This work was funded by a Radiological Society of North America Research Medical Student Grant (#RMS2013) and supported by the Amazon Web Services Diagnostic Development Initiative

Development of a long-acting direct-acting antiviral system for hepatitis C virus treatment in swine

Chronic hepatitis C virus (HCV) infection is a leading cause of cirrhosis worldwide and kills more Americans than 59 other infections, including HIV and tuberculosis, combined. While direct-acting antiviral (DAA) treatments are effective, limited uptake of therapy, particularly in high-risk groups, remains a substantial barrier to eliminating HCV. We developed a long-acting DAA system (LA-DAAS) capable of prolonged dosing and explored its cost-effectiveness. We designed a retrievable coil-shaped LA-DAAS compatible with nasogastric tube administration and the capacity to encapsulate and release gram levels of drugs while resident in the stomach. We formulated DAAs in drug-polymer pills and studied the release kinetics for 1 mo in vitro and in vivo in a swine model. The LA-DAAS was equipped with ethanol and temperature sensors linked via Bluetooth to a phone application to provide patient engagement. We then performed a cost-effectiveness analysis comparing LA-DAAS to DAA alone in various patient groups, including people who inject drugs. Tunable release kinetics of DAAs was enabled for 1 mo with drug-polymer pills in vitro, and the LA-DAAS safely and successfully provided at least month-long release of sofosbuvir in vivo. Temperature and alcohol sensors could interface with external sources for at least 1 mo. The LA-DAAS was cost-effective compared to DAA therapy alone in all groups considered (base case incremental cost-effectiveness ratio $39,800). We believe that the LA-DAA system can provide a cost-effective and patient-centric method for HCV treatment, including in high-risk populations who are currently undertreated.NIH (Grants EB000244 and 5T32DK007191-45