Faculty Bibliography

ABSTRACT: INTRODUCTION: Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children. METHODS: Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis. RESULTS: Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of >/=5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone. CONCLUSIONS: Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27.The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607.

ABSTRACT: INTRODUCTION: The intrinsic heterogeneity of clinical septic shock is a major challenge. For clinical trials, individual patient management, and quality improvement efforts, it is unclear which patients are least likely to survive and thus benefit from alternative treatment approaches. A robust risk stratification tool would greatly aid decision-making. The objective of our study was to derive and test a multi-biomarker-based risk model to predict outcome in pediatric septic shock. METHODS: Twelve candidate serum protein stratification biomarkers were identified from previous genome-wide expression profiling. To derive the risk stratification tool, biomarkers were measured in serum samples from 220 unselected children with septic shock, obtained during the first 24 hours of admission to the intensive care unit. Classification and Regression Tree (CART) analysis was used to generate a decision tree to predict 28-day all-cause mortality based on both biomarkers and clinical variables. The derived tree was subsequently tested in an independent cohort of 135 children with septic shock. RESULTS: The derived decision tree included five biomarkers. In the derivation cohort, sensitivity for mortality was 91% (95% CI 70 - 98), specificity was 86% (80 - 90), positive predictive value was 43% (29 - 58), and negative predictive value was 99% (95 - 100). When applied to the test cohort, sensitivity was 89% (64 - 98) and specificity was 64% (55 - 73). In an updated model including all 355 subjects in the combined derivation and test cohorts, sensitivity for mortality was 93% (79 - 98), specificity was 74% (69 - 79), positive predictive value was 32% (24 - 41), and negative predictive value was 99% (96 - 100). False positive subjects in the updated model had greater illness severity compared to the true negative subjects, as measured by persistence of organ failure, length of stay, and intensive care unit free days. CONCLUSIONS: The pediatric sepsis biomarker risk model (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl) reliably identifies children at risk of death and greater illness severity from pediatric septic shock. PERSEVERE has the potential to substantially enhance clinical decision making, to adjust for risk in clinical trials, and to serve as a septic shock-specific quality metric.

OBJECTIVE: To demonstrate the utility of rescue with veno-arterial extracorporeal membrane oxygenation (VA-ECMO) and to demonstrate the feasibility of administration of chemotherapy during VA-ECMO in a child with an anterior mediastinal mass. DESIGN: Case report. SETTING: Large, tertiary care, pediatric intensive care unit. PATIENT: One patient with cardiopulmonary collapse in the setting of a new diagnosis of an anterior mediastinal mass. INTERVENTIONS: Cardiopulmonary support with VA-ECMO; administration of chemotherapy during VA-ECMO. MEASUREMENTS AND MAIN RESULTS: Successful rescue with VA-ECMO and successful chemotherapy while on VA-ECMO. CONCLUSIONS: VA-ECMO can be successfully used to support patients with cardiopulmonary failure during initial diagnosis of an anterior mediastinal mass. In addition, we have demonstrated that successful chemotherapy can be administered while the patient is supported on VA-ECMO.

Metastasis is the leading cause of treatment failure in medulloblastoma. Understanding the genetic regulation of metastasis may aid in the development of novel treatments. We therefore performed in silico analysis of the mRNA expression of 83 medulloblastomas compiled from two independent microarray studies by focusing on 135 genes most frequently linked to metastasis in other tumors. We then asked whether expression of these genes correlated with metastasis in the medulloblastoma array data sets. We found the platelet-derived growth factor receptor alpha, early growth response protein 1 and insulin-like growth factor 2 genes as well as several genes associated with MYCC and ERBB2 overexpressed by at least 2-fold in metastatic tumors in both array data sets. We conclude that these genes may interact to promote prometastatic signaling in medulloblastoma.