Use of intravenous buprenorphine microdosing to initiate medication for opioid use disorder in a patient with co-occurring pain: case report.

INTRODUCTION: Buprenorphine is used to treat opioid use disorder (OUD). However, therapy is often disrupted during acute pain episodes, and re-initiation is often deferred due to intolerable interruption in opioid analgesics. This case report describes a unique strategy for inducing buprenorphine without stopping opioid analgesics. CASE: One patient with OUD and acute pain was initiated on buprenorphine using intravenous microdosing without precipitated withdrawal or pain exacerbation. Full agonist opioids were successfully weaned after discharge and the patient was linked with a community-based treatment program. CONCLUSION: This case describes use of intravenous buprenorphine to treat OUD and acute pain without adverse consequences.

Peritoneal equilibration testing: Your questions answered.

The peritoneal equilibration test (PET), first described in 1987, is a semiquantitative assessment of peritoneal transfer characteristics in patients undergoing peritoneal dialysis. It is typically performed as a 4-h exchange using 2.27/2.5% dextrose dialysate with serial measurements of blood and dialysate creatinine, urea, and glucose concentrations. The percentage absorption of glucose and D/P creatinine ratio are used to determine peritoneal solute transfer rates. It is used to both help guide peritoneal dialysis prescriptions and to prognosticate. There are several derivative tests which have been described in the literature. In this review, we describe the original PET, the various iterations of the PET, the information gleaned, and the use in the setting of poor solute clearance and in the diagnosis of membrane dysfunction, and limitations of the PET.

A Graphical Toolkit for Longitudinal Dataset Maintenance and Predictive Model Training in Health Care.

BACKGROUND: Predictive analytic models, including machine learning (ML) models, are increasingly integrated into electronic health record (EHR)-based decision support tools for clinicians. These models have the potential to improve care, but are challenging to internally validate, implement, and maintain over the long term. Principles of ML operations (MLOps) may inform development of infrastructure to support the entire ML lifecycle, from feature selection to long-term model deployment and retraining. OBJECTIVES: This study aimed to present the conceptual prototypes for a novel predictive model management system and to evaluate the acceptability of the system among three groups of end users. METHODS: Based on principles of user-centered software design, human-computer interaction, and ethical design, we created graphical prototypes of a web-based MLOps interface to support the construction, deployment, and maintenance of models using EHR data. To assess the acceptability of the interface, we conducted semistructured user interviews with three groups of users (health informaticians, clinical and data stakeholders, chief information officers) and evaluated preliminary usability using the System Usability Scale (SUS). We subsequently revised prototypes based on user input and developed user case studies. RESULTS: Our prototypes include design frameworks for feature selection, model training, deployment, long-term maintenance, visualization over time, and cross-functional collaboration. Users were able to complete 71% of prompted tasks without assistance. The average SUS score of the initial prototype was 75.8 out of 100, translating to a percentile range of 70 to 79, a letter grade of B, and an adjective rating of "good." We reviewed persona-based case studies that illustrate functionalities of this novel prototype. CONCLUSION: The initial graphical prototypes of this MLOps system are preliminarily usable and demonstrate an unmet need within the clinical informatics landscape.