Research
The pediatric sepsis biomarker risk model
1 Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, OH, USA
2 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
3 The EMD Millipore Corporation, St Charles, MO, USA
4 Children's Hospital and Research Center Oakland, Oakland, CA, USA
5 Nationwide Children's Hospital, Columbus, OH, USA
6 Children's Mercy Hospital, Kansas City, MO, USA
7 Penn State Hershey Children's Hospital, Hershey, PA, USA
8 Children's National Medical Center, Washington, DC, USA
9 Children's Hospital of Orange County, Orange, CA, USA
10 Miami Children's Hospital, Miami, FL, USA
11 Texas Children's Hospital, Houston, TX, USA
12 The Children's Hospital of Philadelphia, Philadelphia, PA, USA
13 CS Mott Children's Hospital at the University of Michigan, Ann Arbor, MI, USA
14 Akron Children's Hospital, Akron, OH, USA
15 Morgan Stanley Children's Hospital, Columbia University Medical Center, New York, NY, USA
16 Children's Hospital and Clinics of Minnesota, Minneapolis, MN, USA
17 Children's Hospital of Wisconsin, Milwaukee, WI, USA
18 Primary Children's Medical Center, Salt Lake City, UT, USA
19 St Christopher's Hospital for Children, Philadelphia, PA, USA
20 Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Critical Care 2012, 16:R174 doi:10.1186/cc11652
Published: 1 October 2012Abstract
Introduction
The intrinsic heterogeneity of clinical septic shock is a major challenge. For clinical trials, individual patient management, and quality improvement efforts, it is unclear which patients are least likely to survive and thus benefit from alternative treatment approaches. A robust risk stratification tool would greatly aid decision-making. The objective of our study was to derive and test a multi-biomarker-based risk model to predict outcome in pediatric septic shock.
Methods
Twelve candidate serum protein stratification biomarkers were identified from previous genome-wide expression profiling. To derive the risk stratification tool, biomarkers were measured in serum samples from 220 unselected children with septic shock, obtained during the first 24 hours of admission to the intensive care unit. Classification and Regression Tree (CART) analysis was used to generate a decision tree to predict 28-day all-cause mortality based on both biomarkers and clinical variables. The derived tree was subsequently tested in an independent cohort of 135 children with septic shock.
Results
The derived decision tree included five biomarkers. In the derivation cohort, sensitivity for mortality was 91% (95% CI 70 - 98), specificity was 86% (80 - 90), positive predictive value was 43% (29 - 58), and negative predictive value was 99% (95 - 100). When applied to the test cohort, sensitivity was 89% (64 - 98) and specificity was 64% (55 - 73). In an updated model including all 355 subjects in the combined derivation and test cohorts, sensitivity for mortality was 93% (79 - 98), specificity was 74% (69 - 79), positive predictive value was 32% (24 - 41), and negative predictive value was 99% (96 - 100). False positive subjects in the updated model had greater illness severity compared to the true negative subjects, as measured by persistence of organ failure, length of stay, and intensive care unit free days.
Conclusions
The pediatric sepsis biomarker risk model (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl) reliably identifies children at risk of death and greater illness severity from pediatric septic shock. PERSEVERE has the potential to substantially enhance clinical decision making, to adjust for risk in clinical trials, and to serve as a septic shock-specific quality metric.
