Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling.

We developed and tested a gene expression-based classification method for pediatric septic shock that meets the time constraints of the critical care environment, and can potentially inform therapeutic decisions.

IntroductionThe 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.MethodsTwelve 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.ResultsThe 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.ConclusionsThe 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.

Objectives To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Design Prospective observational study involving microarray-based bioinformatics. Setting Multiple pediatric intensive care units in the United States. Patients Children ≤10 years of age: 18 normal controls, 22 meeting criteria for SIRS, 32 meeting criteria for sepsis, and 67 meeting criteria for septic shock on day 1. The available day 3 samples included 20 patients still meeting sepsis criteria, 39 patients still meeting septic shock criteria, and 24 patients meeting the exclusive day 3 category, SIRS resolved. Interventions None other than standard care. Measurements and Main Results Longitudinal analyses were focused on gene expression relative to control samples and patients having paired day 1 and day 3 samples. The longitudinal analysis focused on up-regulated genes revealed common patterns of up-regulated gene expression, primarily corresponding to inflammation and innate immunity, across all patient groups on day 1. These patterns of up-regulated gene expression persisted on day 3 in patients with septic shock, but not to the same degree in the other patient classes. The longitudinal analysis focused on down-regulated genes demonstrated gene repression corresponding to adaptive immunity-specific signaling pathways and was most prominent in patients with septic shock on days 1 and 3. Gene network analyses based on direct comparisons across the SIRS, sepsis, and septic shock spectrum, and all available patients in the database, demonstrated unique repression of gene networks in patients with septic shock corresponding to major histocompatibility complex antigen presentation. Finally, analyses focused on repression of genes corresponding to zinc-related biology demonstrated that this pattern of gene repression is unique to patients with septic shock. Conclusions Although some common patterns of gene expression exist across the pediatric SIRS, sepsis, and septic shock spectrum, septic shock is particularly characterized by repression of genes corresponding to adaptive immunity and zinc-related biology. (Crit Care Med 2009; 37:1558–1566)