Evaluation of mRNA Biomarkers to Identify Risk of Hospital Acquired Infections in Children Admitted to Paediatric Intensive Care Unit

OBJECTIVES: Hospital-acquired infections (HAI) are associated with significant mortality and morbidity and prolongation of hospital stay, adding strain on limited hospital resources. Despite stringent infection control practices some children remain at high risk of developing HAI. The development of biomarkers which could identify these patients would be useful. In this study our objective was to evaluate mRNA candidate biomarkers for HAI prediction in a pediatric intensive care unit. DESIGN: Serial blood samples were collected from patients admitted to pediatric intensive care unit between March and June 2012. Candidate gene expression (IL1B, TNF, IL10, CD3D, BCL2, BID) was quantified using RT-qPCR. Comparisons of relative gene expression between those that did not develop HAI versus those that did were performed using Mann Whitney U-test. PATIENTS: Exclusion criteria were: age <28 days or ≥16 years, expected length of stay < 24 hours, expected survival < 28 days, end-stage renal disease and end-stage liver disease. Finally, 45 children were included in this study. MAIN RESULTS: The overall HAI rate was 30% of which 62% were respiratory infections. Children who developed HAI had a three-fold increase in hospital stay compared to those who did not (27 days versus 9 days, p<0.001). An increased expression of cytokine genes (IL1B and IL10) was observed in patients who developed HAI, as well as a pro-apoptosis pattern (higher expression of BID and lower expression of BCL2). CD3D, a key TCR co-factor was also significantly down-modulated in patients who developed HAI. CONCLUSIONS: To our knowledge, this is the first study of mRNA biomarkers of HAI in the paediatric population. Increased mRNA expressions of anti-inflammatory cytokine and modulation of apoptotic genes suggest the development of immunosuppression in critically ill children. Immune monitoring using a panel of genes may offer a novel stratification tool to identify HAI risk.