Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents.
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Author
Geva, AlonPatel, 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
Journal title
EClinicalMedicineDate Published
2021-08-31Publication Volume
40Publication Begin page
101112
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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 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 (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 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.Citation
Geva A, Patel MM, Newhams MM, Young CC, Son MBF, Kong M, Maddux AB, Hall MW, Riggs BJ, Singh AR, Giuliano JS, Hobbs CV, Loftis LL, McLaughlin GE, Schwartz SP, Schuster JE, Babbitt CJ, Halasa NB, Gertz SJ, Doymaz S, Hume JR, Bradford TT, Irby K, Carroll CL, McGuire JK, Tarquinio KM, Rowan CM, Mack EH, Cvijanovich NZ, Fitzgerald JC, Spinella PC, Staat MA, Clouser KN, Soma VL, Dapul H, Maamari M, Bowens C, Havlin KM, Mourani PM, Heidemann SM, Horwitz SM, Feldstein LR, Tenforde MW, Newburger JW, Mandl KD, Randolph AG; Overcoming COVID-19 Investigators. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine. 2021 Oct;40:101112. doi: 10.1016/j.eclinm.2021.101112. Epub 2021 Aug 31. PMID: 34485878; PMCID: PMC8405351.DOI
10.1016/j.eclinm.2021.101112ae974a485f413a2113503eed53cd6c53
10.1016/j.eclinm.2021.101112
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