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.
| Original language | English (US) |
|---|---|
| Article number | 101112 |
| Journal | iScience |
| Volume | 40 |
| DOIs | |
| State | Published - Oct 2021 |
Bibliographical note
Funding Information:This work was funded by the US Centers for Disease Control and Prevention (75D30120C07725) and National Institutes of Health ( K12HD047349 and R21HD095228 ).
Funding Information:
All authors report receiving funding from the Centers for Disease Control and Prevention for the current study. AG reports receiving grants from the NIH outside of the submitted work. ABM reports receiving grants from the Francis Family Foundation and from the NIH/NICHD (K23HD096018) outside of the submitted work. CVH reports receiving consulting fees from DYNAMED and BIOFIRE outside of the submitted work. JES reports receiving grants from Merck outside of the submitted work. NBH reports receiving grants from Sanofi, Quidel, the NIH, and the CDC; consulting fees from Moderna; and an educational grant from Genetech outside of the submitted work. CMR reports receiving grants from the NIH/NHLBI (K23HL150244) outside of the submitted work. NZC reports receiving grants or contracts from Boston Children's Hospital and Cincinnati Children's Hospital and Medical Center outside of the submitted work. JCF reports receiving grants from the NIH outside of the submitted work. HD reports payments from Delex Pharma International Inc. outside of the submitted work. PMM reports receiving grants from the NIH and serving as a member of data safety monitoring board for the NIH supported KIDS-DOT trial outside of the submitted work. AGR reports receiving royalties from UpToDate outside of the submitted work. All other authors have nothing to declare.
Publisher Copyright:
© 2021 The Authors
Keywords
- COVID-19
- Clustering
- Critical care medicine
- Multisystem inflammatory syndrome
- Pediatrics
