Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Wong HR

PROVIDER: S-EPMC3196776 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Wong Hector R HR Cvijanovich Natalie Z NZ Allen Geoffrey L GL Thomas Neal J NJ Freishtat Robert J RJ Anas Nick N Meyer Keith K Checchia Paul A PA Lin Richard R Shanley Thomas P TP Bigham Michael T MT Wheeler Derek S DS Doughty Lesley A LA Tegtmeyer Ken K Poynter Sue E SE Kaplan Jennifer M JM Chima Ranjit S RS Stalets Erika E Basu Rajit K RK Varisco Brian M BM Barr Frederick E FE

Critical care medicine 20111101 11

<h4>Objective</h4>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.<h4>Design</h4>Prospective observational study involving microarray-based bioin ...[more]

PMID: 21705885

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Project description:Rationale: We previously generated genome-wide expression data in children with septic shock, based on whole blood-derive RNA, having the potential to lead the field into novel areas of investigation. Objective: Herein we seek to validate our data through a bioinformatic approach centered on a validation patient cohort. Methods: Microarray- and bioinformatics-centered analyses involving our previous data as a training data set (n = 42) and a new, validation cohort (n = 30) as the test data set. Measurements and Main Results: Class prediction modeling using the training data set and the previously reported genome-wide expression signature of pediatric septic shock correctly identified 93 to 100% of 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 72% 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 less than 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 learning 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. Conclusions: These data demonstrate that the previously reported genome-wide expression signature of pediatric septic shock is applicable to a validation cohort of patients. Experiment Overall Design: Table 1: Clinical and demographic data for all subjects in test data set. Experiment Overall Design: Controls Septic Shock Experiment Overall Design: No. of individual subjects 15 30 Experiment Overall Design: Mean age (years) Â± S.D. 3.1 Â± 3.5 3.2 Â± 2.9 Experiment Overall Design: Mean PRISM Score Â± S.D. n/a 18.9 Â± 12.3 Experiment Overall Design: Gender (Male/Female) 8/7 16/14 Experiment Overall Design: Race (no.) A.A./Black (6) A.A./Black (2) Experiment Overall Design: Asian (4) White (26) White (5) Unreported (2)

Project description:ImportanceThe Society of Critical Care Medicine Pediatric Sepsis Definition Task Force sought to develop and validate new clinical criteria for pediatric sepsis and septic shock using measures of organ dysfunction through a data-driven approach.ObjectiveTo derive and validate novel criteria for pediatric sepsis and septic shock across differently resourced settings.Design, setting, and participantsMulticenter, international, retrospective cohort study in 10 health systems in the US, Colombia, Bangladesh, China, and Kenya, 3 of which were used as external validation sites. Data were collected from emergency and inpatient encounters for children (aged <18 years) from 2010 to 2019: 3 049 699 in the development (including derivation and internal validation) set and 581 317 in the external validation set.ExposureStacked regression models to predict mortality in children with suspected infection were derived and validated using the best-performing organ dysfunction subscores from 8 existing scores. The final model was then translated into an integer-based score used to establish binary criteria for sepsis and septic shock.Main outcomes and measuresThe primary outcome for all analyses was in-hospital mortality. Model- and integer-based score performance measures included the area under the precision recall curve (AUPRC; primary) and area under the receiver operating characteristic curve (AUROC; secondary). For binary criteria, primary performance measures were positive predictive value and sensitivity.ResultsAmong the 172 984 children with suspected infection in the first 24 hours (development set; 1.2% mortality), a 4-organ-system model performed best. The integer version of that model, the Phoenix Sepsis Score, had AUPRCs of 0.23 to 0.38 (95% CI range, 0.20-0.39) and AUROCs of 0.71 to 0.92 (95% CI range, 0.70-0.92) to predict mortality in the validation sets. Using a Phoenix Sepsis Score of 2 points or higher in children with suspected infection as criteria for sepsis and sepsis plus 1 or more cardiovascular point as criteria for septic shock resulted in a higher positive predictive value and higher or similar sensitivity compared with the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria across differently resourced settings.Conclusions and relevanceThe novel Phoenix sepsis criteria, which were derived and validated using data from higher- and lower-resource settings, had improved performance for the diagnosis of pediatric sepsis and septic shock compared with the existing IPSCC criteria.

Dataset Information

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

Objective

Design

Setting

Patients

Interventions

Measurements and main results

Conclusions

Publications

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

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