Immune checkpoint inhibitor-mediated hypophysitis: no place like home.

Ambulatory emergency care forms a fundamental part of the strategy of trying to ensure safe and sustainable acute care services. Immune checkpoint inhibitor(ICI)-mediated hypophysitis is an important life-threatening complication of therapy. Patients presenting with clinical features and findings consistent with ICI-mediated hypophysitis were considered in the current study. In the absence of severe features (sodium <125 mmol/L, hypotension, reduced consciousness, hypoglycaemia and/or visual field defect), patients were administered a single intravenous dose of hydrocortisone (100 mg), observed for at least 4 h and then discharged on oral hydrocortisone (20 mg, 10 mg and 10 mg). Patients were then seen urgently in the endocrinology outpatient setting for further management. Fourteen patients (median age 64, 10 male) were managed using the pathway. All patients had biochemically confirmed adrenocorticotropic hormone (ACTH) deficiency. Seven of the 14 were treated with combination ICI therapy, with four having pan-anterior hypopituitarism. There were no 30-day readmissions or any associated hypophysitis-related mortality. All patients continued ICI therapy without interruption.

Detecting drug diversion in health-system data using machine learning and advanced analytics.

PURPOSE: The theft of drugs from healthcare facilities, also known as drug diversion, occurs frequently but is often undetected. This paper describes a research study to develop and test novel drug diversion detection methods. Improved diversion detection and reduction in diversion improves patient safety, limits harm to the person diverting, reduces the public health impact of substance use disorder, and mitigates significant liability risk to pharmacists and their organizations. METHODS: Ten acute care inpatient hospitals across 4 independent health systems extracted 2 datasets from various health information technology systems. Both datasets were consolidated, normalized, classified, and sampled to provide a harmonious dataset for analysis. Supervised machine learning methods were iteratively used on the initial sample dataset to train algorithms to classify medication movement transactions as involving a low or high risk of diversion. Thereafter, the resulting machine learning model classified the risk of diversion in a historical dataset capturing 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians and that included 22 known, blinded diversion cases to measure when the model would have detected the diversion compared to when the diversion was actually detected by existing methods. RESULTS: The machine learning model had 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity in detecting transactions involving a high risk of diversion using the initial sample dataset. In subsequent testing using the much larger historical dataset, the analytics detected known diversion cases (n = 22) in blinded data faster than existing detection methods (a mean of 160 days and a median of 74 days faster; range, 7-579 days faster). CONCLUSION: The study showed that (1) consolidated datasets and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. Users of the technology also noted improved investigation efficiency.