Natalie Z. Cvijanovich

Serum neutrophil gelatinase-associated lipocalin (ngal) as a marker of acute kidney injury in critically ill children with septic shock

Objective:To validate serum neutrophil gelatinase-associated lipocalin (NGAL) as an early biomarker for acute kidney injury in critically ill children with septic shock. Design:Observational cohort study. Setting:Fifteen North American pediatric intensive care units (PICUs). Patients:A total of 143 critically ill children with systemic inflammatory response syndrome (SIRS) or septic shock and 25 healthy controls. Interventions:None. Measurements and Main Results:Serum NGAL was measured during the first 24 hrs of admission to the PICU. Acute kidney injury was defined as a blood urea nitrogen concentration >100 mg/dL, serum creatinine >2 mg/dL in the absence of preexisting renal disease, or the need for dialysis. There was a significant difference in serum NGAL between healthy children (median 80 ng/mL, interquartile ratio [IQR] 55.5–85.5 ng/mL), critically ill children with SIRS (median 107.5 ng/mL, IQR 89–178.5 ng/mL), and critically ill children with septic shock (median 302 ng/mL, IQR 151–570 ng/mL; p < .001). Acute kidney injury developed in 22 of 143 (15.4%) critically ill children. Serum NGAL was significantly increased in critically ill children with acute kidney injury (median 355 ng/mL, IQR 166–1322 ng/mL) compared with those without acute kidney injury (median 186 ng/mL, IQR 98–365 ng/mL; p = .009). Conclusions:Serum NGAL is a highly sensitive but nonspecific predictor of acute kidney injury in critically ill children with septic shock. Further validation of serum NGAL as a biomarker of acute kidney injury in this population is warranted.

Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum

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.

Identification of pediatric septic shock subclasses based on genome-wide expression profiling

BackgroundSeptic 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.MethodsGenome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization.ResultsThree putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the three putative subclasses (analysis of variance, Bonferonni correction, P < 0.05) identified 6,934 differentially regulated genes. K-means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the three subclasses. Leave one out cross-validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C.ConclusionGenome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.

Interleukin-8 as a Stratification Tool for Interventional Trials Involving Pediatric Septic Shock

RATIONALEnInterventional clinical trials involving children with septic shock would benefit from an efficient preenrollment stratification strategy.nnnOBJECTIVESnTo test the predictive value of interleukin (IL)-8 for 28-day mortality in pediatric septic shock.nnnMETHODSnA training data set (n = 40) identified a serum IL-8 of greater than 220 pg/ml as having a 75% sensitivity and specificity for predicting 28-day mortality. This cutoff was then subjected to a series of validation steps.nnnMEASUREMENTS AND MAIN RESULTSnSubjects were drawn from two large, independent pediatric septic shock databases. Prospective application of the IL-8 cutoff to validation data set 1 (n = 139) demonstrated 78% sensitivity and 64% specificity for 28-day mortality. A serum IL-8 level of 220 pg/ml or less, however, had a negative predictive value for 28-day mortality of 95% in validation data set 1, which was subsequently applied to an independently generated data set of children with septic shock (validation set 2, n = 193). A serum IL-8 level of 220 pg/ml or less had a negative predictive value for 28-day mortality of 94% when applied to validation set 2.nnnCONCLUSIONSnA serum IL-8 level of 220 pg/ml or less, obtained within 24 hours of admission, predicts a high likelihood of survival in children with septic shock. We propose that IL-8 can be used to exclude such patients from interventional clinical trials and ultimately derive a study population with a more favorable risk to benefit ratio when subjected to a study agent.

Developing a clinically feasible personalized medicine approach to pediatric septic shock.

RATIONALEnUsing microarray data, we previously identified gene expression-based subclasses of septic shock with important phenotypic differences. The subclass-defining genes correspond to adaptive immunity and glucocorticoid receptor signaling. Identifying the subclasses in real time has theranostic implications, given the potential for immune-enhancing therapies and controversies surrounding adjunctive corticosteroids for septic shock.nnnOBJECTIVESnTo develop and validate a real-time subclassification method for septic shock.nnnMETHODSnGene expression data for the 100 subclass-defining genes were generated using a multiplex messenger RNA quantification platform (NanoString nCounter) and visualized using gene expression mosaics. Study subjects (nu2009=u2009168) were allocated to the subclasses using computer-assisted image analysis and microarray-based reference mosaics. A gene expression score was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (nu2009=u2009132).nnnMEASUREMENTS AND MAIN RESULTSnThe NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The gene expression score identified this subclass with an area under the curve of 0.98 (95% confidence interval [CI95]u2009=u20090.96-0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (odds ratio = 2.7; CI95 = 1.2-6.0; P = 0.016), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (odds ratiou2009=u20094.1; CI95u2009=u20091.4-12.0; Pu2009=u20090.011).nnnCONCLUSIONSnWe 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.

Zinc supplementation in critically ill patients: a key pharmaconutrient?

The purpose of the present paper is to provide a rationale for zinc supplementation as a potential therapeutic agent in critically ill patients by describing its role in health and disease, conducting a systematic review of current randomized trials in critical care, considering optimum route and dose of administration, and making recommendations for future research. Normal zinc homeostasis is required for a functional immune system, adequate antioxidant capacity, glucose homeostasis, and wound healing. In addition, zinc is a required cofactor for many enzymes, transcription factors, and replication factors. In non-critically ill patients, zinc supplementation has been associated with an improvement in markers of immune function. In critically ill patients, only 4 randomized trials have examined the effect of zinc supplementation on clinical outcomes. When all 4 studies were aggregated, zinc supplementation was associated with a nonsignificant reduction in mortality (relative risk = 0.63, 95% confidence intervals 0.25-1.59, P = .33) and length of stay in intensive care (-0.35 days, -0.85 to 0.15; P = .17). Thus, because of the paucity of clinical data, there is inadequate evidence to recommend the routine use of high-dose zinc supplementation in the critically ill. A first step would be to determine the optimal dose that has a maximal positive effect on underlying inflammatory, immunologic, and metabolic processes yet is safe and tolerated by critically ill patients. Subsequently, large, rigorously designed, randomized trials are required to elucidate the efficacy of such doses of zinc supplementation in this patient population.

The pediatric sepsis biomarker risk model

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.

Validating the genomic signature of pediatric septic shock

We previously generated genome-wide expression data (microarray) from children with septic shock having the potential to lead the field into novel areas of investigation. Herein we seek to validate our data through a bioinformatic approach centered on a validation patient cohort. Forty-two children with a clinical diagnosis of septic shock and 15 normal controls served as the training data set, while 30 separate children with septic shock and 14 separate normal controls served as the test data set. Class prediction modeling using the training data set and the previously reported genome-wide expression signature of pediatric septic shock correctly identified 95-100% of controls and septic shock patients in the test data set, depending on the class prediction algorithm and the gene selection method. Subjecting the test data set to an identical filtering strategy as that used for the training data set, demonstrated 75% concordance between the two gene lists. Subjecting the test data set to a purely statistical filtering strategy, with highly stringent correction for multiple comparisons, demonstrated <50% concordance with the previous gene filtering strategy. However, functional analysis of this statistics-based gene list demonstrated similar functional annotations and signaling pathways as that seen in the training data set. In particular, we validated that pediatric septic shock is characterized by large-scale repression of genes related to zinc homeostasis and lymphocyte function. These data demonstrate that the previously reported genome-wide expression signature of pediatric septic shock is applicable to a validation cohort of patients.

The influence of developmental age on the early transcriptomic response of children with septic shock.

Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. Genome-wide expression patterns can provide molecular granularity of the host response and offer insight into why large variations in outcomes exist. We derived whole-blood genome-wide expression patterns within 24 h of PICU admission from children with septic shock. We compared the transcriptome between septic shock developmental-age groups defined as neonates (≤28 d, n = 17), infants (1 month to 1 year, n = 62), toddlers (2–5 years, n = 54) and school-age (≥6 years, n = 47) and age-matched controls. Direct intergroup comparisons demonstrated profound changes in neonates, relative to older children. Neonates with septic shock demonstrated reduced expression of genes representing key pathways of innate and adaptive immunity. In contrast to the largely upregulated transcriptome in all other groups, neonates exhibited a predominantly downregulated transcriptome when compared with controls. Neonates and school-age subjects had the most uniquely regulated genes relative to controls. Age-specific studies of the host response are necessary to identify developmentally relevant translational opportunities that may lead to improved sepsis outcomes.

Validation of a gene expression-based subclassification strategy for pediatric septic shock

Objective:Septic shock heterogeneity has important implications for clinical trial implementation and patient management. We previously addressed this heterogeneity by identifying three putative subclasses of children with septic shock based exclusively on a 100-gene expression signature. Here we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Design:Prospective observational study involving microarray-based bioinformatics. Setting:Multiple pediatric intensive care units in the United States. Patients:Separate derivation (n = 98) and validation (n = 82) cohorts of children with septic shock. Interventions:None other than standard care. Measurements and Main Results:Gene expression mosaics of the 100 class-defining genes were generated for 82 individual patients in the validation cohort. Using computer-based image analysis, patients were classified into one of three subclasses (“A,” “B,” or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. After subclassification, the clinical database was mined for phenotyping. Subclass A patients had higher illness severity relative to subclasses B and C as measured by maximal organ failure, fewer intensive care unit-free days, and a higher Pediatric Risk of Mortality score. Patients in subclass A were characterized by repression of genes corresponding to adaptive immunity and glucocorticoid receptor signaling. Separate subclass assignments were conducted by 21 individual clinicians using visual inspection. The consensus classification of the clinicians had modest agreement with the computer algorithm. Conclusions:We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses have relevant clinical differences.