Multisystem Inflammatory Syndrome of Children: Subphenotypes, Risk Factors, Biomarkers, Cytokine Profiles, and Viral Sequencing.

OBJECTIVE: To assess demographic, clinical, and biomarker features distinguishing patients with multisystem inflammatory syndrome in children (MIS-C); compare MIS-C sub-phenotypes; identify cytokine biosignatures; and characterize viral genome sequences. STUDY DESIGN: We performed a prospective observational cohort study of 124 children hospitalized and treated under the institutional MIS-C Task Force protocol from March to September 2020 at Children's National, a quaternary freestanding children's hospital in Washington, DC. Of this cohort, 63 of the patients had the diagnosis of MIS-C (39 confirmed, 24 probable) and 61 were from the same cohort of admitted patients who subsequently had an alternative diagnosis (controls). RESULTS: Median age and sex were similar between MIS-C and controls. Black (46%) and Latino (35%) children were over-represented in the MIS-C cohort, with Black children at greatest risk (OR 4.62, 95% CI 1.151-14.10; P = .007). Cardiac complications were more frequent in critically ill patients with MIS-C (55% vs 28%; P = .04) including systolic myocardial dysfunction (39% vs 3%; P = .001) and valvular regurgitation (33% vs 7%; P = .01). Median cycle threshold was 31.8 (27.95-35.1 IQR) in MIS-C cases, significantly greater (indicating lower viral load) than in primary severe acute respiratory syndrome coronavirus 2 infection. Cytokines soluble interleukin 2 receptor, interleukin [IL]-10, and IL-6 were greater in patients with MIS-C compared with controls. Cytokine analysis revealed subphenotype differences between critically ill vs noncritically ill (IL-2, soluble interleukin 2 receptor, IL-10, IL-6); polymerase chain reaction positive vs negative (tumor necrosis factor-α, IL-10, IL-6); and presence vs absence of cardiac abnormalities (IL-17). Phylogenetic analysis of viral genome sequences revealed predominance of GH clade originating in Europe, with no differences comparing patients with MIS-C with patients with primary coronavirus disease 19. Treatment was well tolerated, and no children died. CONCLUSIONS: This study establishes a well-characterized large cohort of MIS-C evaluated and treated following a standardized protocol and identifies key clinical, biomarker, cytokine, viral load, and sequencing features. Long-term follow-up will provide opportunity for future insights into MIS-C and its sequelae.

Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

COVID-19 has joined the long list of sexually dimorphic human disorders. Higher lethality in men, evident in the first reports from China, was confirmed in the subsequent Italian outbreak. Newspapers and scientific journals commented on this finding and the preexisting conditions, biological processes, and behavioral differences that may underlie it. However, little appeared to be released about sex differences in severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests performed. Systematic analysis of official websites for 20 countries and 6 US states revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful reported cases by sex. None of the other characteristics, including deaths, were stratified by sex at the time. Beyond suboptimal sex disaggregation, we found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information. Our analysis revealed a wide range of sex ratios among confirmed cases. In countries where a male bias was initially reported, the proportion of women dramatically increased in 3 weeks. Analysis also revealed a complex pattern of sex ratio variation with age. Accurate, peer-reviewed, analysis of harmonized, sex-disaggregated data for characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities, will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by governments.