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).
【저자키워드】 COVID-19, Multisystem inflammatory syndrome, pediatrics, Clustering, Critical care medicine, 【초록키워드】 Inflammation, Hospitalized, severe COVID-19, Pneumonia, children, Diagnosis, prevention, MIS-C, Prevalence, sensitivity, Characteristics, Chest, Patient, Clusters, Control, Cluster, phenotype, age, Other, patients, funding, clinical feature, pulmonary disease, inflammatory biomarkers, Inflammatory, SARS-CoV-2 PCR, criteria, Consensus, Clinicians, clinician, PCR positive, syndrome, help, center, acute COVID-19, finding, positive SARS-CoV-2 PCR, positive PCR, approach, feature, likelihood, recursive feature elimination, tested, identify, performed, healthy, condition, less, assigned, predominant, retained, pulmonary infiltrate, 【제목키워드】 children, Inflammatory, syndrome, acute COVID-19, feature, identify,