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

Abstract

ObjectivesHospital-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.DesignSerial 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.PatientsExclusion 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 ResultsThe 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.ConclusionsTo 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.

Occurrence of marked sepsis-induced immunosuppression in pediatric septic shock: a pilot study

“…At the same time, children who later developed a secondary acquired infection had higher plasma cytokine concentrations (i.e., IL-6, IL-8, and TNF-α). This is in agreement with observations from genomic studies that have found that early mRNA expression modulations of cytokines and apoptotic genes were associated with deleterious outcomes (mortality, secondary infections) [ 29 , 30 ]. Moreover, to the best of our knowledge, this is the first pediatric study which reported correlation between initial cytokine storm and alteration of innate immunity, represented by loss of mHLA-DR.…”

Evaluation of BCL2 and TNFα as mRNA biomarkers for monitoring the immune response in critically ill children

“…We observed that surgical patients were more liable to HAI than medical and septic patients, but in previous studies, the risk was equal, with a minimal increase in complications in septic patients [ 12 , [23] , [24] , [25] ].…”

“…In previous studies, HAI rate ranged from 30% to 55% and pneumonia was the most common HAI. Blood culture positive results ranged from 35% to 55%, the most commonly isolated species were: Klebsiella pneumoniae , Pseudomonas, Acinetobacter, and Staphylococcus aureus [ 12 , [23] , [24] , [25] ].…”

“…B-cell lymphoma 2 (BCL2) gene, an anti-apoptotic gene, is one of the BCL2 group which regulates the intrinsic pathway of apoptosis [ 9 ]. It was used to detect the enhanced lymphocyte apoptosis and identify patients at higher risk of hospital acquired infection in previous researches [ [10] , [11] , [12] ].…”

Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models

“…Much work done to address the challenge of effective prediction of nosocomial infections. Several techniques using rule-based approaches [18–20], Bayesian Networks [10, 21], Ontologies [22], landmark competing risk prediction models [23], statistical models [24–27], case based reasoning [28, 29], and others [30–33] have been suggested and implemented in the past. The problem of identifying new, unanticipated, and useful patterns in public health surveillance and hospital infection control data is discussed in [19].…”