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) <21...

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Detalles Bibliográficos
Autores principales: Geva, Alon, Patel, Manish M., Newhams, Margaret M., Young, Cameron C., Son, Mary Beth F., Kong, Michele, Maddux, Aline B., Hall, Mark W., Riggs, Becky J., Singh, Aalok R., Giuliano, John S., Hobbs, Charlotte V., Loftis, Laura L., McLaughlin, Gwenn E., Schwartz, Stephanie P., Schuster, Jennifer E., Babbitt, Christopher J., Halasa, Natasha B., Gertz, Shira J., Doymaz, Sule, Hume, Janet R., Bradford, Tamara T., Irby, Katherine, Carroll, Christopher L., McGuire, John K., Tarquinio, Keiko M., Rowan, Courtney M., Mack, Elizabeth H., Cvijanovich, Natalie Z., Fitzgerald, Julie C., Spinella, Philip C., Staat, Mary A., Clouser, Katharine N., Soma, Vijaya L., Dapul, Heda, Maamari, Mia, Bowens, Cindy, Havlin, Kevin M., Mourani, Peter M., Heidemann, Sabrina M., Horwitz, Steven M., Feldstein, Leora R., Tenforde, Mark W., Newburger, Jane W., Mandl, Kenneth D., Randolph, Adrienne G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8405351/
https://www.ncbi.nlm.nih.gov/pubmed/34485878
http://dx.doi.org/10.1016/j.eclinm.2021.101112

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