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Review
. 2011 Mar;12(2):165-73.
doi: 10.1097/PCC.0b013e3181e28876.

Biomarker discovery and development in pediatric critical care medicine

Jennifer M Kaplan  1 , Hector R Wong
Affiliations
Review

Biomarker discovery and development in pediatric critical care medicine

Jennifer M Kaplan et al. Pediatr Crit Care Med. 2011 Mar.

Abstract

Objectives: To frame the general process of biomarker discovery and development; and to describe a proposal for the development of a multibiomarker-based risk model for pediatric septic shock.

Data source: Narrative literature review and author-generated data.

Data selection: Biomarkers can be grouped into four broad classes, based on the intended function: diagnostic, monitoring, surrogate, and stratification.

Data extraction and synthesis: Biomarker discovery and development requires a rigorous process, which is frequently not well followed in the critical care medicine literature. Very few biomarkers have successfully transitioned from the candidate stage to the true biomarker stage. There is great interest in developing diagnostic and stratification biomarkers for sepsis. Procalcitonin is currently the most promising diagnostic biomarker for sepsis. Recent evidence suggested that interleukin-8 can be used to stratify children with septic shock having a high likelihood of survival with standard care. Currently, there is a multi-institutional effort to develop a multibiomarker-based sepsis risk model intended to predict outcome and illness severity for individual children with septic shock.

Conclusions: Biomarker discovery and development are an important portion of the pediatric critical care medicine translational research agenda. This effort will require collaboration across multiple institutions and investigators. Rigorous conduct of biomarker-focused research holds the promise of transforming our ability to care for individual patients and our ability to conduct clinical trials in a more effective manner.

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Figures

Figure 1
Figure 1
Two-by-two contingency table illustrating calculations for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Figure 2
Figure 2
Hypothetical receiver operating characteristic (ROC) curve. The straight diagonal line represents an ROC curve having an area under the curve (AUC) of 0.5, in which case the test is positive or negative by chance alone. In contrast, the hypothetical ROC curve has an AUC of 0.857.
Figure 3
Figure 3
Venn analysis comparing gene lists A and B. See text for gene list derivations.
See this image and copyright information in PMC

Comment in

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