Data-Driven Clustering Identifies Features Distinguishing Multisystem Inflammatory Syndrome from Acute COVID-19 in Children and Adolescents
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia.
METHODS: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians)clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients.
FINDINGS: Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (
INTERPRETATION: Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C.
Geva, A., Singh, A. R., & Overcoming COVID-19 Investigators. (2021). Data-Driven Clustering Identifies Features Distinguishing Multisystem Inflammatory Syndrome from Acute COVID-19 in Children and Adolescents. EClinicalMedicine, 40, 101112-101112. https://doi.org/10.1016/j.eclinm.2021.101112