Objective: To develop a clinically feasible stratification strategy for pediatric septic shock, using gene expression mosaics and a 100-gene signature representing the first 24 hrs of admission to the pediatric intensive care unit.
Design: Prospective, observational study involving microarray-based bioinformatics.
Setting: Multiple pediatric intensive care units in the United States.
Patients: Ninety-eight children with septic shock.
Interventions: None other than standard care.
Measurements and main results: Patients were classified into three previously published, genome-wide, expression-based subclasses (subclasses A, B, and C) having clinically relevant phenotypic differences. The class-defining 100-gene signature was depicted for each individual patient, using mosaics generated by the Gene Expression Dynamics Inspector (GEDI). Composite mosaics were generated representing the average expression patterns for each of the three subclasses. Nine individual clinicians served as blinded evaluators. Each evaluator was shown the 98 individual patient mosaics and asked to classify each patient into one of the three subclasses, using the composite mosaics as the reference point. The respective sensitivities, specificities, positive predictive values, and negative predictive values of the subclassification strategy were ≥ 4% across the three subclasses. The classification strategy also generated positive likelihood ratios of ≥ 6.8 and negative likelihood ratios of ≤ .2 across the three subclasses. The κ coefficient across all possible interevaluator comparisons was 0.81.
Conclusions: We have provided initial evidence (proof of concept) for a clinically feasible and robust stratification strategy for pediatric septic shock based on a 100-gene signature and gene expression mosaics.