The goal of this large and innovative study is to improve the diagnosis of sepsis in children through assessing the response of the body to infection and inflammation so called “muli-omics” blood analyses. Specifically, these analyses will allow us to understand the biological mechanisms that occur in response sepsis, such as changes in proteins (proteomics) and chemical processes (metabolomics), in addition to the gene expression (transcriptomics). We will identify novel markers which can improve diagnosis and management of sepsis in children. Using machine learning on this unique dataset, we aim characterize the individual response to sepsis. Thereby, we hope to identify new avenues for better, more personalized sepsis management.
Sepsis is defined as the body’s dysregulated response to infection leading to life-threatening organ dysfunction. It remains a leading cause of death in children worldwide. Up to 50% of deaths from sepsis in children occur within 24 hours of presentation to paediatric intensive care units (PICUs), implying an urgent need for rapid diagnostics. Yet, current diagnostic tools remain insufficient to recognize sepsis early. In addition, difficulties in distinguishing bacterial from viral infection risks to lead to unnecessary (or delayed) antibiotic treatment. Furthermore, current laboratory tests do not allow to predict which children will develop organ failure. Finally, at present despite modern medicine we lack personalized approaches to improve outcomes for children with sepsis. Thus, there is an urgent need for innovative strategies to facilitate the accurate and rapid diagnosis of sepsis in critically ill children.