Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. (October 2021)

• Record Type: Journal Article
• Authors: 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.;
• Is Part Of: EClinicalMedicine. Volume 40(2021)
• Publication Date: 2021-10
• Subjects: COVID-19 -- Multisystem inflammatory syndrome -- Pediatrics -- Critical care medicine -- Clustering; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2021.101112
• Languages: English
• ISSNs: 2589-5370

Abstract

Abstract: 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. Funding: This work was funded by the US Centers for Disease Control and Prevention (75D30120C07725) and National Institutes of Health (K12HD047349 and R21HD095228).