Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents
Creators: name: Riggs, Becky J.
name: Singh, Aalok R.
name: Newburger, Jane W.
name: Tenforde, Mark W.
name: Bowens, Cindy
name: McGuire, John K.
name: Son, Mary Beth F.
name: Mourani, Peter M.
name: Randolph, Adrienne G.
name: Rowan, Courtney M.
name: Feldstein, Leora R.
name: Mack, Elizabeth H.
name: Kong, Michele
name: Maamari, Mia
name: Babbitt, Christopher J.
name: Irby, Katherine
name: Hobbs, Charlotte V.
name: Loftis, Laura L.
name: Staat, Mary A.
name: Geva, Alon
name: Overcoming COVID-19 Investigators
name: Patel, Manish M.
name: Hume, Janet R.
name: Havlin, Kevin M.
name: Gertz, Shira J.
name: Schuster, Jennifer E.
name: Cvijanovich, Natalie Z.
name: Mandl, Kenneth D.
name: Soma, Vijaya L.
name: Maddux, Aline B.
name: Hall, Mark W.
name: Fitzgerald, Julie C.
name: McLaughlin, Gwenn E.
name: Schwartz, Stephanie P.
name: Bradford, Tamara T.
name: Heidemann, Sabrina M.
name: Newhams, Margaret M.
name: Young, Cameron C.
name: Clouser, Katharine N.
name: Spinella, Philip C.
name: Tarquinio, Keiko M.
name: Horwitz, Steven M.
name: Doymaz, Sule
name: Halasa, Natasha B.
name: Dapul, Heda
name: Giuliano, John S.
name: Carroll, Christopher L.
Publisher: name: Elsevier
Year: 2021

description: <p>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) &lt;21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among 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 (<em>N</em>&nbsp;=&nbsp;498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7&middot;2&nbsp;&plusmn;&nbsp;0&middot;4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (<em>N</em>&nbsp;=&nbsp;445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7&middot;4&nbsp;&plusmn;&nbsp;2&middot;1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (<em>N</em>&nbsp;=&nbsp;583; 19% labeled MIS-C) were younger (2&middot;8&nbsp;&plusmn;&nbsp;2&middot;0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from 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.</p>