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Developing a clinically feasible personalized medicine approach to pediatric septic shock

  • Hector R. Wong
  • , Natalie Z. Cvijanovich
  • , Nick Anas
  • , Geoffrey L. Allen
  • , Neal J. Thomas
  • , Michael T. Bigham
  • , Scott L. Weiss
  • , Julie Fitzgerald
  • , Paul A. Checchia
  • , Keith Meyer
  • , Thomas P. Shanley
  • , Michael Quasney
  • , Mark Hall
  • , Rainer Gedeit
  • , Robert J. Freishtat
  • , Jeffrey Nowak
  • , Raj S. Shekhar
  • , Shira Gertz
  • , Emily Dawson
  • , Kelli Howard
  • Kelli Harmon, Eileen Beckman, Erin Frank, Christopher J. Lindsell
  • Cincinnati Children's Hospital Medical Center
  • University of Cincinnati
  • UCSF Benioff Children's Hospital Oakland
  • University of California at Irvine
  • Children's Mercy Hospitals and Clinics
  • Pennsylvania State University
  • Akron Children's Hospital
  • The Children's Hospital of Philadelphia
  • Texas Children's Hospital Houston
  • Miami Children's Hospital
  • University of Michigan, Ann Arbor
  • Nationwide Children’s Hospital
  • Medical College of Wisconsin
  • Children's National Medical Center
  • Children's Hospitals and Clinics of Minnesota
  • Riley Hospital for Children
  • Rutgers - The State University of New Jersey, Newark
  • The University of Chicago

Producción científicarevisión exhaustiva

267 Citas (Scopus)

Resumen

Rationale: Using 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. Objectives: To develop and validate a real-time subclassification method for septic shock. Methods: Gene 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 (n = 168) 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 (n = 132). Measurements and Main Results: The 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] = 0.96-0.99). 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 ratio = 4.1; CI95 = 1.4 - 12.0; P = 0.011). Conclusions: 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.

Idioma originalEnglish
Páginas (desde-hasta)309-315
Número de páginas7
PublicaciónAmerican Journal of Respiratory and Critical Care Medicine
Volumen191
N.º3
DOI
EstadoPublished - 1 feb 2015
Publicado de forma externa

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