Zuberi, Eric C.

Hospital medicine offers diverse career opportunities, yet formal guidance for residents pursuing hospitalist careers is often lacking. To address this gap, the Hospital Internal Medicine Interest Group (HIMIG) was launched at an academic institution in the Southeastern United States to provide structured mentorship and professional development. Residents expressing interest completed an entry survey to identify career goals and areas of focus, informing a longitudinal curriculum that included education, leadership, research, quality improvement, and networking. Seventeen residents (≈25% of the IM residency program) joined HIMIG, and 88% completed the initial survey. Education, leadership, and quality improvement were top interests, while mentorship and career guidance were primary motivators. A post-implementation survey revealed all participants met mentors and felt HIMIG supported their career goals. HIMIG demonstrates a flexible, personalized approach to hospitalist career development that may serve as a model for other programs.

Background: Leprosy, caused by Mycobacterium leprae, presents on a spectrum ranging from tuberculoid to lepromatous disease. Borderline lepromatous leprosy represents an unstable immunological state that predisposes patients to immune-mediated reactions, including erythema nodosum leprosum (ENL), a severe inflammatory complication. Case Presentation: We report a case of a 62-year-old female with borderline lepromatous leprosy who presented with recurrent facial cellulitis and later developed disseminated ENL. She was initially diagnosed following a series of facial infections and confirmatory skin biopsy. Months later, she developed systemic inflammatory lesions consistent with ENL, requiring hospitalization. She was treated with high-dose corticosteroids for ENL and methotrexate to treat type 1 reaction and continued multidrug therapy (MDT) with minocycline, rifampin, and clarithromycin for leprosy, which led to significant clinical improvement. Conclusion: This case highlights the diagnostic challenges of leprosy in the United States and the importance of recognizing ENL as a severe immunologic complication requiring prompt intervention. A multidisciplinary approach is essential for optimal patient outcomes.

Purpose: To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in critically ill patients receiving propofol sedation. Methods: Patients from 11 intensive care units (ICUs) across five Mayo Clinic hospitals were included if they met the following criteria: a) ≥ 18 years of age, b) received propofol infusion while on invasive mechanical ventilation for ≥24 h, and c) had a triglyceride level measured. The primary outcome was hypertriglyceridemia (triglyceride >400 mg/dL) onset within 10 days of propofol initiation. Both COVID-inclusive and COVID-independent modeling pipelines were developed to ensure applicability post-pandemic. Decision thresholds were chosen to maintain model sensitivity >80%. Nested leave-one-site-out cross-validation (LOSO-CV) was used to externally evaluate pipeline performance. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP). Results: Among 3922 included patients, 769 (19.6%) developed propofol-associated hypertriglyceridemia, and 879 (22.4%) had COVID-19 at ICU admission. During nested LOSO-CV, the COVID-inclusive pipeline achieved an average AUC-ROC of 0.71 (95% confidence interval [CI] 0.70–0.72), while the COVID-independent pipeline achieved an average AUC-ROC of 0.69 (95% CI 0.68–0.70). Age, initial propofol dose, and BMI were the top three most important features in both models. Conclusion: We developed an explainable ML-based tool with acceptable predictive performance for assessing the risk of propofol-associated hypertriglyceridemia in ICU patients. This tool can aid clinicians in identifying at-risk patients to guide triglyceride monitoring and optimize sedative selection.