Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents

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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Main Authors: Alon Geva, Manish M. Patel, Margaret M. Newhams, Cameron C. Young, Mary Beth F. Son, Michele Kong, Aline B. Maddux, Mark W. Hall, Becky J. Riggs, Aalok R. Singh, John S. Giuliano, Charlotte V. Hobbs, Laura L. Loftis, Gwenn E. McLaughlin, Stephanie P. Schwartz, Jennifer E. Schuster, Christopher J. Babbitt, Natasha B. Halasa, Shira J. Gertz, Sule Doymaz, Janet R. Hume, Tamara T. Bradford, Katherine Irby, Christopher L. Carroll, John K. McGuire, Keiko M. Tarquinio, Courtney M. Rowan, Elizabeth H. Mack, Natalie Z. Cvijanovich, Julie C. Fitzgerald, Philip C. Spinella, Mary A. Staat, Katharine N. Clouser, Vijaya L. Soma, Heda Dapul, Mia Maamari, Cindy Bowens, Kevin M. Havlin, Peter M. Mourani, Sabrina M. Heidemann, Steven M. Horwitz, Leora R. Feldstein, Mark W. Tenforde, Jane W. Newburger, Kenneth D. Mandl, Adrienne G. Randolph
Format: Article
Language:English
Published: Elsevier 2021-10-01
Series:EClinicalMedicine
Subjects:
COVID-19
Multisystem inflammatory syndrome
Pediatrics
Critical care medicine
Clustering
Online Access:http://www.sciencedirect.com/science/article/pii/S2589537021003928

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http://www.sciencedirect.com/science/article/pii/S2589537021003928

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