{"doi":"10.1101/2022.09.26.22280364","title":"Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program","abstract":"Abstract 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. Research in context Evidence before this study We searched PubMed and preprint articles from December 2019, to July 2022, for studies published in English that investigated the clinical subphenotypes of MIS-C using the terms “multi-system inflammatory syndrome in children” or “pediatric inflammatory multisystem syndrome” and “phenotypes”. Most previous research described the symptoms, clinical characteristics and risk factors associated with MIS-C and how these differ from acute COVID-19, Kawasaki Disease and Toxic Shock Syndrome. One single-center study of 63 patients conducted in 2020 divided patients into Kawasaki and non-Kawasaki disease subphenotypes. Another CDC study evaluated 3 subclasses of MIS-C in 570 children, with one class representing the highest number of organ systems, a second class with predominant respiratory system involvement, and a third class with features overlapping with Kawasaki Disease. However, this study evaluated cases from March to July 2020, during the early phase of the pandemic when misclassification of cases as Kawasaki disease or acute COVID-19 may have occurred. Therefore, it is not known from the existing literature whether the presentation of MIS-C has changed with newer variants such as delta and omicron. Added value of this study PEDSnet provides one of the largest MIS-C cohorts described so far, providing sufficient power for detailed analyses on MIS-C subphenotypes. Our analyses span the entire length of the pandemic, including the more recent omicron wave, and provide an update on the presentations of MIS-C and its temporal dynamics. We found that children have a spectrum of illness that can be characterized as mild (lower inflammatory markers, fewer organ systems involved), moderate (4-6 organ involvement with clinical overlap with acute COVID-1","journal":"medRxiv","year":2022,"id":298056,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9606,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":986651,"name":"Naimin Jing","orcid":"0000-0003-3103-4329","position":1,"is_corresponding":false},{"id":767888,"name":"Xiaokang Liu","orcid":"0000-0001-6920-5598","position":2,"is_corresponding":false},{"id":984528,"name":"Vitaly Lorman","orcid":"0000-0003-0561-6587","position":3,"is_corresponding":false},{"id":722260,"name":"Mitchell Maltenfort","orcid":"0000-0002-0141-1720","position":4,"is_corresponding":false},{"id":381553,"name":"Julia Schuchard","orcid":"0000-0002-8613-8961","position":5,"is_corresponding":false},{"id":381373,"name":"Qiong Wu","orcid":"0000-0002-6063-0800","position":6,"is_corresponding":false},{"id":300699,"name":"Jiayi Tong","orcid":"0000-0002-1213-6699","position":7,"is_corresponding":false},{"id":675068,"name":"Hanieh Razzaghi","orcid":"0000-0001-5001-1590","position":8,"is_corresponding":false},{"id":32980,"name":"Asunción Mejías","orcid":"0000-0002-5983-8006","position":9,"is_corresponding":false},{"id":225720,"name":"Grace M. Lee","orcid":"0000-0002-0051-2466","position":10,"is_corresponding":false},{"id":675067,"name":"Nathan M. Pajor","orcid":"0000-0002-1637-4942","position":11,"is_corresponding":false},{"id":297410,"name":"Grant S. Schulert","orcid":"0000-0001-5923-7051","position":12,"is_corresponding":false},{"id":734824,"name":"Deepika Thacker","orcid":"0000-0002-6738-397X","position":13,"is_corresponding":false},{"id":367009,"name":"Ravi Jhaveri","orcid":"0000-0001-9921-0419","position":14,"is_corresponding":false},{"id":675064,"name":"Dimitri Christakis","orcid":"0000-0003-0726-7253","position":15,"is_corresponding":false},{"id":860340,"name":"L. Charles Bailey","orcid":"0000-0002-8967-0662","position":16,"is_corresponding":false},{"id":302148,"name":"Christopher B. Forrest","orcid":"0000-0003-1252-068X","position":17,"is_corresponding":false},{"id":608951,"name":"Yong Chen","orcid":"0000-0003-1612-8351","position":18,"is_corresponding":false},{"id":516763,"name":"Suchitra Rao","orcid":"0000-0002-0334-6301","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:31.270564Z","pmid":"36203555","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}