title: Angiopoietin-1, angiopoietin-2 and bicarbonate as diagnostic biomarkers in children with severe sepsis.
abstract: Severe pediatric sepsis continues to be associated with high mortality rates in children. Thus, an important area of biomedical research is to identify biomarkers that can classify sepsis severity and outcomes. The complex and heterogeneous nature of sepsis makes the prospect of the classification of sepsis severity using a single biomarker less likely. Instead, we employ machine learning techniques to validate the use of a multiple biomarkers scoring system to determine the severity of sepsis in critically ill children. The study was based on clinical data and plasma samples provided by a tertiary care center's Pediatric Intensive Care Unit (PICU) from a group of 45 patients with varying sepsis severity at the time of admission. Canonical Correlation Analysis with the Forward Selection and Random Forests methods identified a particular set of biomarkers that included Angiopoietin-1 (Ang-1), Angiopoietin-2 (Ang-2), and Bicarbonate (HCO[Formula: see text]) as having the strongest correlations with sepsis severity. The robustness and effectiveness of these biomarkers for classifying sepsis severity were validated by constructing a linear Support Vector Machine diagnostic classifier. We also show that the concentrations of Ang-1, Ang-2, and HCO[Formula: see text] enable predictions of the time dependence of sepsis severity in children.
authors.affiliation: Department of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America; Department of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Kun
authors.initials: K
authors.lastname: Wang
authors.affiliation: Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: Vineet
authors.initials: V
authors.lastname: Bhandari
authors.affiliation: Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: John S
authors.initials: JS
authors.lastname: Giuliano
authors.affiliation: Department of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut, United States of America; Department of Applied Physics, Department of Physics, and Graduate Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Corey S
authors.initials: CS
authors.lastname: O Hern
authors.affiliation: Benjamin Levich Institute and Physics Department, The City College of New York, New York, New York, United States of America.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Shattuck
authors.affiliation: Department of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Kirby
mesh_terms: D000367:Age Factors; D042683:Angiopoietin-1 / Q000097:blood*; D042702:Angiopoietin-2 / Q000097:blood*; D001639:Bicarbonates / Q000097:blood*; D015415:Biomarkers / Q000097:blood; D016000:Cluster Analysis; D006801:Humans; D015278:Intensive Care Units, Pediatric; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000097:blood* / Q000175:diagnosis*; D012720:Severity of Illness Index; D060388:Support Vector Machine
chemical_list: D042683:Angiopoietin-1; D042702:Angiopoietin-2; D001639:Bicarbonates; D015415:Biomarkers