Metabolomic and Immunologic Discriminators of MIS-C at Emergency Room Presentation.

Laura A Vella,Amalia Z Berna,Allison M Blatz, Joey Logan, Priya Sharma, Yang Liu, Jonathan Tedesco, Cara Toland, Leena Babiker, Kathryn Hafertepe, Shane Kammerman, Josef Novacek,Elikplim Akaho,Alexander K Gonzalez,Deanne Taylor,Caroline Diorio,Fran Balamuth,Hamid Bassiri, Audrey R Odom John

medRxiv : the preprint server for health sciences(2024)

引用 0|浏览7
暂无评分
摘要
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.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要