ABSTRACT
BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.
Subject(s)
COVID-19 , Mucocutaneous Lymph Node Syndrome , Child , Humans , COVID-19/diagnosis , COVID-19/genetics , Systemic Inflammatory Response Syndrome/diagnosis , Systemic Inflammatory Response Syndrome/genetics , Hospitals , Mucocutaneous Lymph Node Syndrome/diagnosis , Mucocutaneous Lymph Node Syndrome/genetics , COVID-19 TestingABSTRACT
OBJECTIVES: Antibiotic spectrum index (ASI) is a recently developed antimicrobial stewardship (AMS) tool that aims to classify antibiotics based on activity against clinically relevant bacterial pathogens. METHODS: We utilised ASI in a 2-year retrospective study between April 2019 and April 2021 in four paediatric intensive care units of a specialist UK children's hospital to quantify antibiotic use based on age, presence of immunosuppression and AMS input. We then compared ASI to days of therapy (DOT) to determine the utility of this AMS metric. We have made changes to Gerber's original ASI list and score of antibiotics to align with prescribing and resistance patterns in the UK. RESULTS: Median ASI/antibiotic days increased with age: for infants under 1 year of age 4.1 (IQR 4.0-4.3), for children 1-5 years 4.4 (IQR 4.0-4.6) and for children over 5 years 4.5 (IQR 4.1-4.6). Immunocompromised patients received much broader-spectrum antibiotics than immunocompetent patients throughout the whole study period. Patients who had AMS input had a higher ASI compared with those who did not throughout the whole period, likely due to more complex patients being discussed on such rounds. CONCLUSIONS: Our results show a complex picture of changing antibiotic consumption and prescribing in a large specialist paediatric hospital in the UK with a long-standing AMS programme before and throughout the COVID-19 pandemic. ASI shows less variability than DOT and can potentially be used to identify patient groups and time periods where broader-spectrum antibiotics are used to help guide further AMS efforts.