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.

IntroductionThe REGAL (RSV [respiratory syncytial virus] Evidence—a Geographical Archive of the Literature) series provides a comprehensive review of the published evidence in the field of RSV in Western countries over the last 20 years. This first of seven publications covers the epidemiology and burden of RSV infection.MethodsA systematic review was undertaken for articles published between Jan 1, 1995 and Dec 31, 2015 across PubMed, Embase, The Cochrane Library, and Clinicaltrials.gov. Studies reporting data for hospital visits/admissions for RSV infection among children (≤18 years of age), as well as studies reporting RSV-associated morbidity, mortality, and risk factors were included. Study quality and strength of evidence (SOE) were graded using recognized criteria.Result2315 studies were identified of which 98 were included. RSV was associated with 12–63% of all acute respiratory infections (ARIs) and 19–81% of all viral ARIs causing hospitalizations in children (high SOE). Annual RSV hospitalization (RSVH) rates increased with decreasing age and varied by a factor of 2–3 across seasons (high SOE). Studies were conflicting on whether the incidence of RSVH has increased, decreased, or remained stable over the last 20 years (moderate SOE). Length of hospital stay ranged from 2 to 11 days, with 2–12% of cases requiring intensive care unit admission (moderate SOE). Case-fatality rates were <0.5% (moderate SOE). Risk factors associated with RSVH included: male sex; age <6 months; birth during the first half of the RSV season; crowding/siblings; and day-care exposure (high SOE).ConclusionRSV infection remains a major burden on Western healthcare systems and has been associated with significant morbidity. Further studies focusing on the epidemiology of RSV infection (particularly in the outpatient setting), the impact of co-infection, better estimates of case-fatality rates and associated risk factors (all currently moderate/low SOE) are needed to determine the true burden of disease.FundingAbbvie.Electronic supplementary materialThe online version of this article (doi:10.1007/s40121-016-0123-0) contains supplementary material, which is available to authorized users.

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.