BackgroundCerebrospinal fluid (CSF) shunt placement is frequently complicated by bacterial infection. Shunt infection diagnosis relies on bacterial culture of CSF which can often produce false-negative results. Negative cultures present a conundrum for physicians as they are left to rely on other CSF indices, which can be unremarkable. New methods are needed to swiftly and accurately diagnose shunt infections. CSF chemokines and cytokines may prove useful as diagnostic biomarkers. The objective of this study was to evaluate the potential of systemic and CSF biomarkers for identification of CSF shunt infection.MethodsWe conducted a retrospective chart review of children with culture-confirmed CSF shunt infection at Children’s Hospital and Medical Center from July 2013 to December 2015. CSF cytokine analysis was performed for those patients with CSF in frozen storage from the same sample that was used for diagnostic culture.ResultsA total of 12 infections were included in this study. Patients with shunt infection had a median C-reactive protein (CRP) of 18.25 mg/dL. Median peripheral white blood cell count was 15.53 × 103 cells/mL. Those with shunt infection had a median CSF WBC of 332 cells/mL, median CSF protein level of 406 mg/dL, and median CSF glucose of 35.5 mg/dL. An interesting trend was observed with gram-positive infections having higher levels of the anti-inflammatory cytokine interleukin (IL)-10 as well as IL-17A and vascular endothelial growth factor (VEGF) compared to gram-negative infections, although these differences did not reach statistical significance. Conversely, gram-negative infections displayed higher levels of the pro-inflammatory cytokines IL-1β, fractalkine (CX3CL1), chemokine ligand 2 (CCL2), and chemokine ligand 3 (CCL3), although again these were not significantly different. CSF from gram-positive and gram-negative shunt infections had similar levels of interferon gamma (INF-γ), tumor necrosis factor alpha (TNF-α), IL-6, and IL-8.ConclusionsThis pilot study is the first to characterize the CSF cytokine profile in patients with CSF shunt infection and supports the distinction of chemokine and cytokine profiles between gram-negative and gram-positive infections. Additionally, it demonstrates the potential of CSF chemokines and cytokines as biomarkers for the diagnosis of shunt infection.

Children’s hospitals responded to COVID-19 by limiting nonurgent healthcare encounters, conserving personal protective equipment, and restructuring care processes to mitigate viral spread. We assessed year-over-year trends in healthcare encounters and hospital charges across US children’s hospitals before and during the COVID-19 pandemic. We performed a retrospective analysis, comparing healthcare encounters and inflation-adjusted charges from 26 tertiary children’s hospitals reporting to the PROSPECT database from February 1 to June 30 in 2019 (before the COVID-19 pandemic) and 2020 (during the COVID-19 pandemic). All children’s hospitals experienced similar trends in healthcare encounters and charges during the study period. Inpatient bed-days, emergency department visits, and surgeries were lower by a median 36%, 65%, and 77%, respectively, per hospital by the week of April 15 (the nadir) in 2020 compared with 2019. Across the study period in 2020, children’s hospitals experienced a median decrease of $276 million in charges.

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BACKGROUND: In several states, payers penalize hospitals when an inpatient readmission follows an inpatient stay. Observation stays are typically excluded from readmission calculations. Previous studies suggest inconsistent use of observation designations across hospitals. We sought to describe variation in observation stays and examine the impact of inclusion of observation stays on readmission metrics. METHODS: We conducted a retrospective cohort study of hospitalizations at 50 hospitals contributing to the Pediatric Health Information System database from January 1, 2018, to December 31, 2018. We examined prevalence of observation use across hospitals and described changes to inpatient readmission rates with higher observation use. We described 30-day inpatient-only readmission rates and ranked hospitals against peer institutions. Finally, we included observation encounters into the calculation of readmission rates and evaluated hospitals’ change in readmission ranking. RESULTS: Most hospitals (n = 44; 88%) used observation status, with high variation in use across hospitals (0%–53%). Readmission rate after index inpatient stay (6.8%) was higher than readmission after an index observation stay (4.4%), and higher observation use by hospital was associated with higher inpatient-only readmission rates. When compared with peers, hospital readmission rank changed with observation inclusion (60% moving at least 1 quintile). CONCLUSIONS: The use of observation status is variable among children’s hospitals. Hospitals that more liberally apply observation status perform worse on the current inpatient-to-inpatient readmission metric, and inclusion of observation stays in the calculation of readmission rates significantly affected hospital performance compared with peer institutions. Consideration should be given to include all admission types for readmission rate calculation.