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. 2010 Oct;38(10):1955-61.
doi: 10.1097/CCM.0b013e3181eb924f.

Toward a clinically feasible gene expression-based subclassification strategy for septic shock: proof of concept

Hector R Wong  1 , Derek S Wheeler, Ken Tegtmeyer, Sue E Poynter, Jennifer M Kaplan, Ranjit S Chima, Erika Stalets, Rajit K Basu, Lesley A Doughty
Affiliations

Toward a clinically feasible gene expression-based subclassification strategy for septic shock: proof of concept

Hector R Wong et al. Crit Care Med. 2010 Oct.

Abstract

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.

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Figures

Figure 1
Figure 1
Composite mosaics for sub-classes A, B, and C. The composite mosaics represent the average expression patterns of patients in each of the 3 respective sub-classes and served as the reference point for the cross validation procedures.
Figure 2
Figure 2
Examples of individual mosaics for patients in sub-classes A, B, or C. The evaluators viewed 98 individual patient mosaics and classified each patient into one of the three sub-classes using the composite mosaics in Figure 1 as the reference point.
Figure 3
Figure 3
A, Mosaics for 2 patients in original sub-class B that were incorrectly classified as sub-class A. Six of the 9 evaluators classified example 1 as sub-class A, and 7 of the 9 evaluators classified example 2 as sub-class A. B, Mosaics for 2 patients in original sub-class C that were incorrectly classified as sub-class A. All of the 9 evaluators classified example 1 as sub-class A, and 8 of the 9 evaluators classified example 2 as sub-class A.
Figure 4
Figure 4
Mosaics for 4 patients in original sub-class B that were incorrectly classified as sub-class C. Eight of the 9 evaluators classified example 1 as sub-class C. Seven of the 9 evaluators classified example 2 as sub-class C. Five of the 9 evaluators classified example 3 as sub-class C. Seven of the 9 evaluators classified example 4 as sub-class C.
Figure 5
Figure 5
Mosaics for 2 patients that did not receive at least 5 “votes” for any 1 of the 3 sub-classes and therefore remained unclassified.
See this image and copyright information in PMC

References

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