Pediatric Hospital Medicine Fellow The Children's Hospital at Montefiore Long Island city, New York, United States
Background: Pediatric encephalitis is an uncommon clinical syndrome caused by a variety of etiologies and may be associated with significant morbidity and mortality. Existing literature on prognostic indicators in encephalitis is limited by utilization of mixed pediatric and adult cohorts, execution prior to neuronal antibody and/or routine multiplex nucleic acid testing for infectious etiologies, emergence of new pathogens, or small sample sizes. Thus, a contemporary review of the epidemiology of pediatric encephalitis and risk factors for increased disease severity is needed. Objective: Our goal is to identify the infectious, immune-mediated, and other etiologies and risk factors associated with poor functional neurologic outcomes in acute pediatric encephalitis. We hypothesize that pediatric patients admitted for acute encephalitis who present with status epilepticus, require ICU admission, or have a delayed time to definitive diagnosis (i.e., greater than the median time) will have increased rates of poor hospital outcomes including discharge to an acute rehabilitation facility or death. Design/Methods: This is a retrospective cohort study that utilizes a shared encephalitis database from 3 New York City pediatric tertiary care centers. The study includes 137 patients aged 60 days to 18 years who were admitted between January 1, 2010 and December 31, 2019 with a probable or definitive diagnosis of encephalitis or meningoencephalitis.
Institutional Review Board approval from all 3 participating pediatric hospitals and data use agreements between the centers were obtained prior to initiation of the study. Data collection is currently ongoing.
All statistical analyses will be conducted by Dr. Hoang-Wu with assistance from a biostatistician. Patient characteristics will be summarized using standard summary statistics, and the associations between risk factors and poor hospital outcomes will be examined using bivariate analyses and multivariable logistic regression.
11/2023: completion of data collection 12/2023: data analysis 2/2024: completion of abstract and manuscript
