Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children (preprint)

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection. The highly diverse clinical features of MIS-C necessities characterizing its features by subphenotypes for improved recognition and treatment. However, jointly identifying subphenotypes in multi-site settings can be challenging. We propose a distributed multi-site latent class analysis (dMLCA) approach to jointly learn MIS-C subphenotypes using data across multiple institutions. Methods We used data from the electronic health records (EHR) systems across nine U.S. childrens hospitals. Among the 3,549,894 patients, we extracted 864 patients < 21 years of age who had received a diagnosis of MIS-C during an inpatient stay or up to one day before admission. Using MIS-C conditions, laboratory results, and procedure information as input features for the patients, we applied our dMLCA algorithm and identified three MIS-C subphenotypes. As validation, we characterized and compared more granular features across subphenotypes. To evaluate the specificity of the identified subphenotypes, we further compared them with the general subphenotypes identified in the COVID-19 infected patients. Findings Subphenotype 1 (46.1%) represents patients with a mild manifestation of MIS-C not requiring intensive care, with minimal cardiac involvement. Subphenotype 2 (25.3%) is associated with a high risk of shock, cardiac and renal involvement, and an intermediate risk of respiratory symptoms. Subphenotype 3 (28.6%) represents patients requiring intensive care, with a high risk of shock and cardiac involvement, accompanied by a high risk of >4 organ system being impacted. Importantly, for hospital-specific clinical decision-making, our algorithm also revealed a substantial heterogeneity in relative proportions of these three subtypes across hospitals. Properly accounting for such heterogeneity can lead to accurate characterization of the subphenotypes at the patient-level. Interpretation Our identified three MIS-C subphenotypes have profound implications for personalized treatment strategies, potentially influencing clinical outcomes. Further, the proposed algorithm facilitates federated subphenotyping while accounting for the heterogeneity across hospitals.

Associations between area-level health-related social factor indices and risk of acute COVID-19: An EHR-based cohort study from the RECOVER program (preprint)

Background: Research demonstrates that SARS-CoV-2 infection (COVID-19) among adults disproportionately impacts racial and ethnic minorities and those living in lower-income communities. Similar research in children is limited due, in part, to the relatively low incidence in children compared to adults. This analysis, conducted as part of the RECOVER Initiative, explores this question. Methods: Electronic health record (EHR) data from PEDSnet, a multi-institutional research network of pediatric healthcare organizations, were geocoded and linked to two indices of contextual social deprivation: the Area Deprivation Index and the Child Opportunity Index. Univariate statistics were employed to test the association between each index and COVID19 positivity among children ages 0-20 tested at one of six Childrens hospitals. Multivariate logistic regression was used to explore the relationship between these social context indices and racial disparities in positivity, controlling co-variates. Results: Both ADI and COI were significantly associated with COVID-19 positivity in univariate and adjusted models, particularly in the pre-delta and delta variant waves. ADI showed a stronger association. Higher rates of positivity were found for non-Hispanic Black, Hispanic, and multi-racial children compared to non-Hispanic White children. These racial disparities remained significant after control for either index and other variables. Conclusion: ADI and COI are significantly associated with COVID-19 test positivity in a population of children and adolescents tested in childrens hospital settings. These social contextual variables do not fully explain racial disparities arguing that racial disparities are not solely a reflection of socioeconomic status. Future disparities research should consider both race and social context.

Changes in Treatment and Severity of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program (preprint)

Objectives The purpose of this study was to examine how the treatment and severity of multisystem inflammatory syndrome in children (MIS-C) has changed over more than two years of the COVID-19 pandemic in the United States. Methods Electronic health record data were retrieved from the PEDSnet network as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative. The study included data for children ages 0 to 20 years hospitalized for MIS-C from March 1, 2020 through July 20, 2022. Descriptive statistics for MIS-C treatments and laboratory results were computed for three time periods of interest: March 1, 2020 to May 31, 2021 (pre-Delta); June 1 to December 31, 2021 (primarily Delta); January 1 to July 20, 2022 (primarily Omicron). Standardized differences measured the effect size of the difference between Omicron and pre-Omicron cohorts. Results The study included 946 children with a diagnosis of MIS-C. The largest differences in the Omicron period compared to prior years were decreases in the percentage of children with abnormal troponin (effect size = 0.40), abnormal lymphocytes (effect size = 0.33), and intensive care unit (ICU) visits (effect size = 0.34). There were small decreases in the Omicron period for the majority of treatments and abnormal laboratory measurements examined, including infliximab, anticoagulants, furosemide, aspirin, IVIG without steroids, echocardiograms, mechanical ventilation, platelets, ferritin, and sodium. Conclusions This study provides the first evidence that the severity of MIS-C declined in the first half of the year 2022 relative to prior years of the COVID-19 pandemic in the United States.

Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program (preprint)

Background: Multi-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters. Methods: We conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns. Findings: We identified 1186 children hospitalized with MIS-C. The highest proportion of children (44.4%) were aged between 5-11 years, with a male predominance (61.0%), and non-Hispanic white ethnicity (40.2%). Most (67.8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72.5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51.1% of children early in the pandemic, which decreased to 33.9% from the pre-delta period to the omicron period. Interpretation: MIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time.

Leveraging serology testing to identify children at risk for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the RECOVER program (preprint)

ABSTRACT The impact of post-acute sequelae of SARS-CoV-2 infection (PASC) in children is underrecognized. We developed an EHR-based algorithm across eight pediatric institutions to identify children with COVID-19 based on serology testing from 3/2020 through 4/2022 who had not been identified by PCR. Overall, serology tests were used 100-fold less than PCR. Seroprevalence of IgG anti-nucleocapsid antibodies remained stable, while rates of positive IgG anti-spike antibodies increased in teenagers after COVID-19 vaccine approval. Through data harmonization and after excluding 1,410 serology test results that may have been influenced by vaccines, we identified 2,714 children that were COVID-19 positive exclusively by serology. These patients were frequently tested as inpatients (24% vs. 2%), had chronic conditions more frequently (37% vs 24%), and a MIS-C diagnosis (23% vs. <1%) compared with PCR-positive children. Identification of children that could have been paucisymptomatic, not tested, or missed is critical to define the burden of PASC in children.