Phenotypic Classification of Multisystem Inflammatory Syndrome in Children Using Latent Class Analysis

Area of Science:

• Pediatric critical care medicine
• Infectious diseases
• Epidemiology

Background:

• Multisystem inflammatory syndrome in children (MIS-C) is a severe hyperinflammatory condition following SARS-CoV-2 infection.
• MIS-C presentation varies, and risk factors for severity differ, complicating diagnosis and management.
• Characterizing MIS-C phenotypes is crucial for accurate classification and identifying high-risk patients.

Purpose of the Study:

• To identify distinct phenotypic clusters of MIS-C.
• To determine if these clusters are associated with increased clinical severity.
• To analyze the temporal distribution of MIS-C clusters.

Main Methods:

• Latent class analysis was applied to a large US national surveillance cohort of MIS-C cases.
• Twenty-nine clinical signs and symptoms were used for clustering.
• Retrospective analysis of cases with symptom onset by December 31, 2022.

Main Results:

• Three MIS-C clusters were identified: respiratory (8.0%), shock and cardiac (37.6%), and undifferentiated (54.5%).
• The shock and cardiac cluster exhibited the highest ICU admission rate (82.3%) and prolonged ICU stays.
• The proportion of respiratory and shock/cardiac clusters decreased after the Omicron variant emerged.

Conclusions:

• MIS-C cases form three distinct subgroups with varying clinical phenotypes and severity.
• These identified clusters can inform surveillance case definitions and risk stratification for severe outcomes.
• Understanding MIS-C phenotypes is essential for targeted interventions and improved patient outcomes.