Objective: To create models for prediction and benchmarking of pediatric intensive care unit (PICU) length of stay (LOS) for patients with critical bronchiolitis.
Hypothesis: We hypothesize that machine learning models applied to an administrative database will be able to accurately predict and benchmark the PICU LOS for critical bronchiolitis.
Design: Retrospective cohort study.
Objective: The incidence of deterioration and associated characteristics are largely unknown for children transported for admission from referring emergency departments (EDs) to general inpatient units. This study describes this population and identifies associated preadmission characteristics.
Methods: This single-center cohort study included children ≤ 18 years old transferred from an ED and directly admitted to general inpatient units from 2016 to 2019.