Unintentional buprenorphine and methadone poisoning in children: a matched observational study

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS)

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning.

Buprenorphine and its formulations: a comprehensive review

Buprenorphine, a novel long-acting analgesic, was developed with the intention of two purposes: analgesia and opioid use disorder. Regarding its pharmacodynamics, it is a partial agonist at mu receptors, an inverse agonist at kappa receptors, and an antagonist at delta receptors. For the purpose of analgesia, three formulations of buprenorphine were developed: IV/IM injectable formulation (Buprenex®), transdermal patch formulation (Butrans®), and buccal film formulation (Belbuca®). Related to opioid dependence, the formulations developed were subcutaneous extended release (Sublocade®), subdermal implant (Probuphine®), and sublingual tablets (Subutex®). Lastly, in order to avoid misuse of buprenorphine for opioid dependence, two combination formulations paired with naloxone were developed: film formulation (Suboxone®) and tablet formulation (Zubsolv®). In this review, we present details of each formulation along with their similarities and differences between each other and clinical considerations.

Lessons from the First Wave of COVID-19 for Improved Medications for Opioid Use Disorder (MOUD) Treatment: Benefits of Easier Access, Extended Take Homes, and New Delivery Modalities

Background Medications for Opioid Use Disorder (MOUD) are associated with important public health benefits. Program changes implemented in response to COVID-19 hold promise as ongoing strategies to improve MOUD treatment. Methods: MOUD patients on buprenorphine or methadone, providers, government regulators, and persons who use drugs not in MOUD were recruited in the Northeast region of the United States between June and October of 2020 via advertisements, fliers, and word of mouth. Semi-structured qualitative interviews were conducted. Interviews were professionally transcribed and thematically coded by two independent coders. Results: We conducted interviews with 13 people currently on buprenorphine, 11 currently on methadone, 3 previously on buprenorphine, 4 previously on methadone, and 6 who used drugs but had never been on MOUD. In addition, we interviewed MOUD providers, clinic staff, and government officials at agencies that regulate MOUD. Most participants found increased take-home doses, home medication delivery, and telehealth implemented during COVID-19 to be favorable, reporting that these program changes reduced travel time to clinics, facilitated retention in care, and reduced stigma associated with clinic attendance. However, some participants reported negative consequences of COVID-19, most notably, decreased access to basic resources, such as food, clothing, and harm reduction materials that had previously been distributed at some MOUD clinics. Conclusion: Access to and retention in MOUD can be lifesaving for persons using drugs. COVID-19-impelled program changes, including increased take-home doses, home medication delivery, and telehealth generally improved participants' experiences with MOUD. Making these permanent could improve retention in care.