Early liver transplantation (LT) in European centers reportedly improved survival in patients with severe alcoholic hepatitis (AH) not responding to medical therapy. Our aim was to determine if a strategy of early LT for severe AH could be applied successfully in the United States. We reviewed 111 patients with severe AH at our center from January 2012 to January 2015. The primary end point was mortality at 6 months or early LT, with a secondary end point of alcohol relapse after LT. Survival was compared between those receiving early LT and matched patients who did not. Using a process similar to the European trial, 94 patients with severe AH not responding to medical therapy were evaluated for early LT. Overall, 9 (9.6%) candidates with favorable psychosocial profiles underwent early LT, comprising 3% of all adult LT during the study period. The 6-month survival rate was higher among those receiving early LT compared with matched controls (89% vs. 11%, p<0.001). Eight recipients are alive at a median of 735 days with 1 alcohol relapse. Early LT for severe AH can achieve excellent clinical outcomes with low impact on the donor pool and low rates of alcohol relapse in highly selected patients in the United States.

Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified. Conclusions: Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

Objective The epidemiology of psychiatric symptoms among COVID-19 patients is poorly characterized. This paper seeks to identify the prevalence of anxiety, depression, and acute stress disorder among hospitalized patients with COVID-19. Methods Adult patients recently admitted to non-ICU medical ward settings with COVID-19 were eligible for enrollment. Enrolled patients were screened for depression, anxiety, and delirium. Subsequently, patients were followed by phone after two weeks and re-screened for depression, anxiety, and acute stress disorder symptoms. Subjects’ medical records were abstracted for clinical data. Results 58 subjects were enrolled of whom 44 completed the study. Initially, 36% of subjects had elevated anxiety symptoms and 29% elevated depression symptoms. At two-week follow-up, 9% had elevated anxiety symptoms, 20% elevated depression symptoms, and 25% mild-to-moderate acute stress disorder symptoms. Discharge to home was not associated with improvement in psychiatric symptoms. Conclusion A significant number of patients hospitalized with COVID-19 experience symptoms of depression and anxiety. While anxiety improves following index admission, depression remains fairly stable. Furthermore, a significant minority of patients experience acute stress disorder symptoms, though these are largely mild-to-moderate.

Electronic health records (EHRs) offer significant advantages over paper charts, such as ease of portability, facilitated communication, and a decreased risk of medical errors; however, important ethical concerns related to patient confidentiality remain. Although legal protections have been implemented, in practice, EHRs may be still prone to breaches that threaten patient privacy. Potential safeguards are essential, and have been implemented especially in sensitive areas such as mental illness, substance abuse, and sexual health. Features of one institutional model are described that may illustrate the efforts to both ensure adequate transparency and ensure patient confidentiality. Trust and the therapeutic alliance are critical to the provider-patient relationship and quality healthcare services. All of the benefits of an EHR are only possible if patients retain confidence in the security and accuracy of their medical records.

No abstract