title: Which biomarkers reveal neonatal sepsis?
abstract: We address the identification of optimal biomarkers for the rapid diagnosis of neonatal sepsis. We employ both canonical correlation analysis (CCA) and sparse support vector machine (SSVM) classifiers to select the best subset of biomarkers from a large hematological data set collected from infants with suspected sepsis from Yale-New Haven Hospital's Neonatal Intensive Care Unit (NICU). CCA is used to select sets of biomarkers of increasing size that are most highly correlated with infection. The effectiveness of these biomarkers is then validated by constructing a sparse support vector machine diagnostic classifier. We find that the following set of five biomarkers capture the essential diagnostic information (in order of importance): Bands, Platelets, neutrophil CD64, White Blood Cells, and Segs. Further, the diagnostic performance of the optimal set of biomarkers is significantly higher than that of isolated individual biomarkers. These results suggest an enhanced sepsis scoring system for neonatal sepsis that includes these five biomarkers. We demonstrate the robustness of our analysis by comparing CCA with the Forward Selection method and SSVM with LASSO Logistic Regression.
authors.affiliation: Department of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America ; Department of Mechanical Engineering and Materials Science, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Kun
authors.initials: K
authors.lastname: Wang
authors.affiliation: Division of Perinatal Medicine, 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 Mathematics, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Sofya
authors.initials: S
authors.lastname: Chepustanova
authors.affiliation: Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California, United States of America.
authors.forename: Greg
authors.initials: G
authors.lastname: Huber
authors.affiliation: Department of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Stephen
authors.initials: S
authors.lastname: O'Hara
authors.affiliation: Department of Mechanical Engineering & Materials Science, Department of Applied Physics, and Department of Physics, 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: D015415:Biomarkers / Q000378:metabolism*; D006801:Humans; D016015:Logistic Models; D018805:Sepsis / Q000175:diagnosis* / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers