Hector R Wong1,2, Natalie Z Cvijanovich3, Nick Anas4, Geoffrey L Allen5, Neal J Thomas6, Michael T Bigham7, Scott L Weiss8, Julie C Fitzgerald8, Paul A Checchia9, Keith Meyer10, Michael Quasney11, Mark Hall12, Rainer Gedeit13, Robert J Freishtat14, Jeffrey Nowak15, Shekhar S Raj16, Shira Gertz17, Jocelyn R Grunwell18, Christopher J Lindsell19. 1. 1 Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, Ohio. 2. 2 Department of Pediatrics and. 3. 3 UCSF Benioff Children's Hospital Oakland, Oakland, California. 4. 4 Children's Hospital of Orange County, Orange, California. 5. 5 Children's Mercy Hospital, Kansas City, Missouri. 6. 6 Penn State Children's Hospital, Hershey, Pennsylvania. 7. 7 Akron Children's Hospital, Akron, Ohio. 8. 8 The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania. 9. 9 Texas Children's Hospital and Baylor College of Medicine, Houston, Texas. 10. 10 Miami Children's Hospital, Miami, Florida. 11. 11 C. S. Mott Children's Hospital at the University of Michigan, Ann Arbor, Michigan. 12. 12 Nationwide Children's Hospital, Columbus, Ohio. 13. 13 Children's Hospital of Wisconsin, Milwaukee, Wisconsin. 14. 14 Children's National Health System, Washington, District of Columbia. 15. 15 Children's Hospitals and Clinics of Minnesota, Minneapolis, Minnesota. 16. 16 Riley Hospital for Children, Indianapolis, Indiana. 17. 17 Joseph M. Sanzari Children's Hospital, Hackensack University Medical Center, Hackensack, New Jersey; and. 18. 18 Children's Healthcare of Atlanta at Egleston, Atlanta, Georgia. 19. 19 Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio.
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.
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.
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Authors: Hector R Wong; Scott L Weiss; John S Giuliano; Mark S Wainwright; Natalie Z Cvijanovich; Neal J Thomas; Geoffrey L Allen; Nick Anas; Michael T Bigham; Mark Hall; Robert J Freishtat; Anita Sen; Keith Meyer; Paul A Checchia; Thomas P Shanley; Jeffrey Nowak; Michael Quasney; Arun Chopra; Julie C Fitzgerald; Rainer Gedeit; Sharon Banschbach; Eileen Beckman; Patrick Lahni; Kimberly Hart; Christopher J Lindsell Journal: PLoS One Date: 2014-01-29 Impact factor: 3.240
Authors: Hector R Wong; Scott L Weiss; John S Giuliano; Mark S Wainwright; Natalie Z Cvijanovich; Neal J Thomas; Geoffrey L Allen; Nick Anas; Michael T Bigham; Mark Hall; Robert J Freishtat; Anita Sen; Keith Meyer; Paul A Checchia; Thomas P Shanley; Jeffrey Nowak; Michael Quasney; Arun Chopra; Julie C Fitzgerald; Rainer Gedeit; Sharon Banschbach; Eileen Beckman; Kelli Harmon; Patrick Lahni; Christopher J Lindsell Journal: PLoS One Date: 2014-03-13 Impact factor: 3.240
Authors: Ruchir Gupta; Mara L Leimanis; Marie Adams; André S Bachmann; Katie L Uhl; Caleb P Bupp; Nicholas L Hartog; Eric J Kort; Rosemary Olivero; Sarah S Comstock; Dominic J Sanfilippo; Sophia Y Lunt; Jeremy W Prokop; Surender Rajasekaran Journal: Am J Physiol Lung Cell Mol Physiol Date: 2021-04-14 Impact factor: 6.011
Authors: Hector R Wong; Ron W Reeder; Russell Banks; Robert A Berg; Kathleen L Meert; Mark W Hall; Patrick S McQuillen; Peter M Mourani; Ranjit S Chima; Samuel Sorenson; James W Varni; Julie McGalliard; Jerry J Zimmerman Journal: Pediatr Crit Care Med Date: 2021-01-01 Impact factor: 3.971