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Improved risk stratification in pediatric septic shock using both protein and mRNA Biomarkers: Persevere-XP

  • Hector R. Wong
  • , Natalie Z. Cvijanovich
  • , Nick Anas
  • , Geoffrey L. Allen
  • , Neal J. Thomas
  • , Michael T. Bigham
  • , Scott L. Weiss
  • , Julie C. Fitzgerald
  • , Paul A. Checchia
  • , Keith Meyer
  • , Michael Quasney
  • , Mark Hall
  • , Rainer Gedeit
  • , Robert J. Freishtat
  • , Jeffrey Nowak
  • , Shekhar S. Raj
  • , Shira Gertz
  • , Jocelyn R. Grunwell
  • , 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
  • Baylor College of Medicine
  • 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
  • Children's Healthcare of Atlanta

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Rationale: We previously derived and validated the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate baseline mortality risk in children with septic shock. The PERSEVERE biomarkers are serum proteins selected from among the proteins directly related to 80 mortality risk assessment genes. The initial approach to selecting the PERSEVERE biomarkers left 68 genes unconsidered. Objectives: To determine if the 68 previously unconsidered genes can improve upon the performance of PERSEVERE and to provide biological information regarding the pathophysiology of septic shock. Methods: We reduced the number of variables by determining the biological linkage of the 68 previously unconsidered genes. The genes identified through variable reduction were combined with the PERSEVERE-based mortality probability to derive a risk stratification model for 28-day mortality using classification and regression tree methodology (n = 307). The derived tree, PERSEVERE-XP, was then tested in a separate cohort (n = 77). Measurements and Main Results: Variable reduction revealed a network consisting of 18 mortality risk assessment genes related to tumor protein 53 (TP53). In the derivation cohort, PERSEVERE-XP had an area under the receiver operating characteristic curve (AUC) of 0.90 (95% confidence interval, 0.85-0.95) for differentiating between survivors and nonsurvivors. In the test cohort, the AUC was 0.96 (95% confidence interval, 0.91-1.0). The AUC of PERSEVERE-XP was superior to that of PERSEVERE. Conclusions: PERSEVERE-XP combines protein and mRNA biomarkers to provide mortality risk stratification with possible clinical utility. PERSEVERE-XP significantly improves on PERSEVERE and suggests a role for TP53-related cellular division, repair, and metabolism in the pathophysiology of septic shock.

Original languageEnglish
Pages (from-to)494-501
Number of pages8
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume196
Issue number4
DOIs
StatePublished - 15 Aug 2017
Externally publishedYes

Keywords

  • Biomarkers
  • Mortality
  • Sepsis
  • Stratification

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