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

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. children's 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.

Leveraging Serologic Testing to Identify Children at Risk For Post-Acute Sequelae of SARS-CoV-2 Infection: An Electronic Health Record-Based Cohort Study from the RECOVER Program.

Using an electronic health record-based algorithm, we identified children with Coronavirus disease 2019 (COVID-19) based exclusively on serologic testing between March 2020 and April 2022. Compared with the 131 537 polymerase chain reaction-positive children, the 2714 serology-positive children were more likely to be inpatients (24% vs 2%), to have a chronic condition (37% vs 24%), and to have a diagnosis of multisystem inflammatory syndrome in children (23% vs <1%). Identification of children who could have been asymptomatic or paucisymptomatic and not tested is critical to define the burden of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 infection in children.