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.

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Objective Septic shock heterogeneity has important implications for clinical trial implementation and patient management. We previously addressed this heterogeneity by identifying 3 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 1 of 3 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 ICU-free days, and a higher PRISM 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.