The Multifaceted Presentation of the Multisystem Inflammatory Syndrome in Children: Data from a Cluster Analysis

Hafize Emine Sönmez1, Şengül Çağlayan2, Gülçin Otar Yener3

  • 1Department of Pediatric Rheumatology, Kocaeli University, Kocaeli 41001, Turkey.

Insights

Multisystem inflammatory syndrome in children (MIS-C) presents with diverse phenotypes, not a single disease. Identifying these distinct groups and their prognostic factors is crucial for individualized patient management and improved outcomes.

Area of Science:

  • Pediatric Rheumatology
  • Infectious Diseases
  • Critical Care Medicine

Background:

  • Multisystem inflammatory syndrome in children (MIS-C) is a serious condition requiring a deeper understanding of its varied presentations.
  • Phenotypic variability in MIS-C suggests it may not be a monolithic disease, necessitating classification for effective treatment.

Purpose of the Study:

  • To evaluate MIS-C patient outcomes based on distinct disease phenotypes.
  • To identify prognostic factors associated with severe courses of MIS-C.

Main Methods:

  • A cross-sectional study involving 293 MIS-C patients from seven pediatric rheumatology centers.
  • Two-step cluster analysis was employed to categorize patients into distinct subgroups based on clinical features.
  • Outcomes and laboratory findings were compared across identified patient clusters.

Main Results:

  • Four MIS-C subgroups were identified: Kawasaki-like, MAS-like, LV dysfunction, and other presentations.
  • The MAS-like and LV dysfunction groups experienced longer fever duration and hospitalization.
  • Severe MIS-C cases showed elevated inflammatory markers, neutrophil-lymphocyte ratio, and cardiac biomarkers, with decreased lymphocyte and platelet counts.

Conclusions:

  • MIS-C exhibits a spectrum of clinical features and outcomes, underscoring its heterogeneity.
  • Recognizing these distinct phenotypes is essential for tailoring individualized management strategies in pediatric patients.
  • Phenotypic classification aids in predicting disease severity and guiding therapeutic interventions.
Abstract

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