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Call For Papers: Computational Modeling of Physiological Systems

Discovery of the gene signature for acute lung injury in patients with sepsis

¹ Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
² Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
³ Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts
⁴ Pulmonary and Critical Care Unit, Massachusetts General Hospital, Boston, Massachusetts
⁵ Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania

ABSTRACT

The acute respiratory distress syndrome (ARDS)/acute lung injury (ALI) was described 30 yr ago, yet making a definitive diagnosis remains difficult. The identification of biomarkers obtained from peripheral blood could provide additional noninvasive means for diagnosis. To identify gene expression profiles that may be used to classify patients with ALI, 13 patients with ALI + sepsis and 20 patients with sepsis alone were recruited from the Medical Intensive Care Unit of the University of Pittsburgh Medical Center, and microarrays were performed on peripheral blood samples. Several classification algorithms were used to develop a gene signature for ALI from gene expression profiles. This signature was validated in an independently obtained set of patients with ALI + sepsis (n = 8) and sepsis alone (n = 1). An eight-gene expression profile was found to be associated with ALI. Internal validation found that the gene signature was able to distinguish patients with ALI + sepsis from patients with sepsis alone with 100% accuracy, corresponding to a sensitivity of 100%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 100%. In the independently obtained external validation set, the gene signature was able to distinguish patients with ALI + sepsis from patients with sepsis alone with 88.9% accuracy. The use of classification models to develop a gene signature from gene expression profiles provides a novel and accurate approach for classifying patients with ALI.

classification; microarray; acute respiratory distress syndrome