University of Virginia Children's Hospital

To determine whether weight-for-age (WFA) centiles on disease-specific growth charts predict clinical outcomes in children with cerebral palsy (CP). We conducted a retrospective cohort study of pediatric patients with CP in Gross Motor Function Classification System levels III to V, treated at a tertiary care hospital from January 2016 to June 2022. Patients were categorized as above or below the 20th centile WFA using CP-specific growth curves. Clinical outcomes included emergency department and urgent care visits, hospital and pediatric intensive care unit (PICU) admissions, fracture incidence, surgical procedures, and mortality. Of 127 patients (62.9% male, age range at study entry 3-20 years; mean age 10 years 7 months, SD 4 years 5 months), 113 were above and 14 were below the 20th centile. The 'below' group had a 122% higher mean number of emergency department and urgent care visits than the 'above' group (p= 0.004, adjusted incidence rate ratio: 2.22; 95% confidence interval: 1.29, 3.8). They also had a median 1.5 more PICU admissions and 5.2 longer PICU days compared to the 'above' group. Mortality was 4.4% (5/113) in the 'above' group and 28.6% (4/14) in the 'below' group. Children with CP who fall below the 20th centile WFA appear to require more healthcare services and may face increased mortality risk. Maintaining WFA above the 20th centile may represent an important clinical target.

In this article, we describe the potential utility and design of chatbots to improve history taking in the pediatric intensive care unit (PICU). The fast-paced, high-stakes environment of the PICU often forces clinicians to obtain only enough information to make immediate clinical decisions. Specific barriers to comprehensive history taking include insufficient time, frequent interruptions, caring for a wide range of conditions, need for timely interventions, and language differences. We propose that chatbots could play a critical role in improving history taking in the PICU by collecting information related to a patient's current presentation and exploring areas that are commonly neglected, such as social histories. To explore the use of chatbots in the PICU setting, we will first describe the current scope of chatbots as medical history taking aids. Next, we will outline specific considerations for the development of chatbots for the PICU, including methods for involving users, such as patients, caregivers, and clinicians directly in the design, mitigating false information, and establishing safeguards for chatbot behavior. Finally, we will review methods to evaluate chatbots. The overall purpose of this perspective article is to 1) propose the PICU as a novel environment where chatbots could improve history taking and diagnostic reasoning and 2) delineate specific user-centric design and evaluation methods.

Environmental enteric dysfunction (EED) is an asymptomatic acquired disorder characterized by upper small bowel inflammation, villus blunting, and gut permeability. It is a major contributor to poor growth in childhood as well as other highly consequential outcomes such as delayed neuorcognitive development. After decades of intermittent interest in this entity, we are now seeing a resurgence in the field of EED. However, recent studies have been hampered by a lack of investigation of the target tissue—the upper small bowel. In 2016, the EEDBI (Environmental Enteric Dysfunction Biopsy Initiative) Consortium was established as a common scientific platform across 3 independent EED biopsy cohort studies in Bangladesh, Pakistan, and Zambia. Two centers in the United States recruited comparison groups of children undergoing endoscopy for clinical indications. The EEDBI Consortium goal was to augment the contributions of the individual centers and answer high-level questions amenable to analysis and interpretation across the studies. Here, we describe the Consortium and its cohorts and recruitment procedures across studies. We also offer details applicable to all papers in this supplement, which describe EED mucosal histology, morphometry, immunohistochemistry, and transcriptomics as well as histology relationship to pathogens and biomarkers.