Distinct Phenotype Clusters in Childhood Inflammatory Brain Diseases: Implications for Diagnostic Evaluation. Issue 3 (March 2014) - Cellucci, Tania; Tyrrell, Pascal N.; Twilt, Marinka; Sheikh, Shehla; Benseler, Susanne M.
<x xml:space="preserve">Abstract</x> Objective To identify distinct clusters of children with inflammatory brain diseases based on clinical, laboratory, and imaging features at presentation, to assess which features contribute strongly to the development of clusters, and to compare additional features between the identified clusters. Methods A single‐center cohort study was performed with children who had been diagnosed as having an inflammatory brain disease between June 1, 1989 and December 31, 2010. Demographic, clinical, laboratory, neuroimaging, and histologic data at diagnosis were collected. K‐means cluster analysis was performed to identify clusters of patients based on their presenting features. Associations between the clusters and patient variables, such as diagnoses, were determined. Results A total of 147 children (50% female; median age 8.8 years) were identified: 105 with primary central nervous system (CNS) vasculitis, 11 with secondary CNS vasculitis, 8 with neuronal antibody syndromes, 6 with postinfectious syndromes, and 17 with other inflammatory brain diseases. Three distinct clusters were identified. Paresis and speech deficits were the most common presenting features in cluster 1. Children in cluster 2 were likely to present with behavior changes, cognitive dysfunction, and seizures, while those in cluster 3 experienced ataxia, vision abnormalities, and seizures. Lesions seen on T2/fluid‐attenuated inversion recovery sequences of magnetic resonance imaging were common in all clusters, but unilateral ischemic lesions were more prominent in cluster 1. The clusters were associated with specific diagnoses and diagnostic test results. Conclusion Children with inflammatory brain diseases presented with distinct phenotypical patterns that are associated with specific diagnoses. This information may inform the development of a diagnostic classification of childhood inflammatory brain diseases and suggest that specific pathways of diagnostic evaluation are warranted.