Metabolomic and Immunologic Discriminators of MIS-C at Emergency Room Presentation.
Laura A VellaAmalia Z BernaAllison M BlatzJoey LoganPriya SharmaYang LiuJonathan TedescoCara TolandLeena BabikerKathryn HafertepeShane KammermanJosef NovacekElikplim AkahoAlexander K GonzalezDeanne TaylorCaroline DiorioFran BalamuthHamid BassiriAudrey R Odom JohnPublished in: medRxiv : the preprint server for health sciences (2024)
Multisystem Inflammatory Syndrome in Childhood (MIS-C) follows SARS-CoV-2 infection and frequently leads to intensive care unit admission. The inability to rapidly discriminate MIS-C from similar febrile illnesses delays treatment and leads to misdiagnosis. To identify diagnostic discriminators at the time of emergency department presentation, we enrolled 104 children who met MIS-C screening criteria, 14 of whom were eventually diagnosed with MIS-C. Before treatment, we collected breath samples for volatiles and peripheral blood for measurement of plasma proteins and immune cell features. Clinical and laboratory features were used as inputs for a machine learning model to determine diagnostic importance. MIS-C was associated with significant changes in breath volatile organic compound (VOC) composition as well as increased plasma levels of secretory phospholipase A2 (PLA2G2A) and lipopolysaccharide binding protein (LBP). In an integrated model of all analytes, the proportion of TCRVβ21.3+ non-naive CD4 T cells expressing Ki-67 had a high sensitivity and specificity for MIS-C, with diagnostic accuracy further enhanced by low sodium and high PLA2G2A. We anticipate that accurate diagnosis will become increasingly difficult as MIS-C becomes less common. Clinical validation and application of this diagnostic model may improve outcomes in children presenting with multisystem febrile illnesses.
Keyphrases
- emergency department
- intensive care unit
- machine learning
- peripheral blood
- case report
- binding protein
- young adults
- public health
- healthcare
- high resolution
- lymph node
- artificial intelligence
- urinary tract infection
- skeletal muscle
- metabolic syndrome
- hiv infected
- mass spectrometry
- sars cov
- immune response
- gas chromatography
- early life
- acute respiratory distress syndrome