Prader–Willi syndrome and autism spectrum disorders: an evolving story

Prader–Willi syndrome (PWS) is well-known for its genetic and phenotypic complexities. Caused by a lack of paternally derived imprinted material on chromosome 15q11–q13, individuals with PWS have mild to moderate intellectual disabilities, repetitive and compulsive behaviors, skin picking, tantrums, irritability, hyperphagia, and increased risks of obesity. Many individuals also have co-occurring autism spectrum disorders (ASDs), psychosis, and mood disorders. Although the PWS 15q11–q13 region confers risks for autism, relatively few studies have assessed autism symptoms in PWS or directly compared social, behavioral, and cognitive functioning across groups with autism or PWS. This article identifies areas of phenotypic overlap and difference between PWS and ASD in core autism symptoms and in such comorbidities as psychiatric disorders, and dysregulated sleep and eating. Though future studies are needed, PWS provides a promising alternative lens into specific symptoms and comorbidities of autism

Predictors of emergency department opioid administration and prescribing: A machine learning approach

© 2020 Introduction: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emergency Department (ED) setting, where providers are now confronted with balancing pain management and potential abuse. This study aims to examine patient sociodemographic and ED clinical characteristics to comprehensively determine predictors of opioid administration during an ED visit (ED-RX) and prescribing upon discharge (DC-RX). Methods: ED visit data of patients ≥18 years old from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2014 to 2017 were used. Opioid prescriptions were determined utilizing Lexicon narcotic drug classifications. Visit characteristics studied included sociodemographic variables, and ED clinical variables, such as chief complaint, and discharge diagnosis. Machine learning methods were used to determine predictors of ED-RX and DC-RX and weighted logistic regressions were performed using selected predictors. Results: Of the 44,227 ED visits identified, patients tended to be female (57.4%), and White (74.2%) with an average age of 46.4 years (SE = 0.3). Weighted proportions of ED-RX and DC-RX were 23.2% and 18.9%, respectively. The strongest predictors of ED-RX were CT scan ordered (OR = 2.18, 95% CI = 1.84–2.58), abdominal pain (OR = 1.93, 95% CI:1.59–2.34) and back pain (OR = 1.81, 95% CI:1.45–2.27). Tooth pain (OR = 6.94, 95% CI = 4.40–10.94) and fracture injury diagnoses (OR = 3.76, 95% CI = 2.72–5.19) were the strongest predictors of DC-RX. Conclusions: These findings demonstrate the utility of machine learning for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines