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

ABSTRACT: 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.

SUBMITTER: Peronnet E

PROVIDER: S-EPMC4807819 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Peronnet Estelle E Nguyen Kha K Cerrato Elisabeth E Guhadasan Rathi R Venet Fabienne F Textoris Julien J Pachot Alexandre A Monneret Guillaume G Carrol Enitan Delphine ED

PloS one 20160325 3

<h4>Objectives</h4>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.<h4> ...[more]

PMID: 27015534

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Project description:BACKGROUND:There is little descriptive data on Stenotrophomonas maltophilia hospital-acquired pneumonia (HAP) in critically ill patients. The optimal modalities of antimicrobial therapy remain to be determined. Our objective was to describe the epidemiology and prognostic factors associated with S. maltophilia pneumonia, focusing on antimicrobial therapy. METHODS:This nationwide retrospective study included all patients admitted to 25 French mixed intensive care units between 2012 and 2017 with hospital-acquired S. maltophilia HAP during intensive care unit stay. Primary endpoint was time to in-hospital death. Secondary endpoints included microbiologic effectiveness and antimicrobial therapeutic modalities such as delay to appropriate antimicrobial treatment, mono versus combination therapy, and duration of antimicrobial therapy. RESULTS:Of the 282 patients included, 84% were intubated at S. maltophilia HAP diagnosis for duration of 11 [5-18] days. The Simplified Acute Physiology Score II was 47 [36-63], and the in-hospital mortality was 49.7%. Underlying chronic pulmonary comorbidities were present in 14.1% of cases. Empirical antimicrobial therapy was considered effective on S. maltophilia according to susceptibility patterns in only 30% of cases. Delay to appropriate antimicrobial treatment had, however, no significant impact on the primary endpoint. Survival analysis did not show any benefit from combination antimicrobial therapy (HR = 1.27, 95%CI [0.88; 1.83], p = 0.20) or prolonged antimicrobial therapy for more than 7 days (HR = 1.06, 95%CI [0.6; 1.86], p = 0.84). No differences were noted in in-hospital death irrespective of an appropriate and timely empiric antimicrobial therapy between mono- versus polymicrobial S. maltophilia HAP (p = 0.273). The duration of ventilation prior to S. maltophilia HAP diagnosis and ICU length of stay were shorter in patients with monomicrobial S. maltophilia HAP (p = 0.031 and p = 0.034 respectively). CONCLUSIONS:S. maltophilia HAP occurred in severe, long-stay intensive care patients who mainly required prolonged invasive ventilation. Empirical antimicrobial therapy was barely effective while antimicrobial treatment modalities had no significant impact on hospital survival. TRIAL REGISTRATION:clinicaltrials.gov, NCT03506191.

Project description:BackgroundsThe in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making.MethodsData were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature.ResultsOverall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model.ConclusionThe ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.

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

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

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