Distinguishing septic shock from non-septic shock in postsurgical patients using gene expression

Abstract

Objectives: To obtain a gene expression signature to distinguish between septic shock and non-septic shock in postoperative patients, since patients with both conditions show similar signs and symptoms.

Methods: Differentially expressed genes were selected by microarray analysis in the discovery cohort. These genes were evaluated by quantitative real time polymerase chain reactions in the validation cohort to determine their reliability and predictive capacity by receiver operating characteristic curve analysis.

Results: Differentially expressed genes selected were IGHG1, IL1R2, LCN2, LTF, MMP8, and OLFM4. The multivariate regression model for gene expression presented an area under the curve value of 0.922. These genes were able to discern between both shock conditions better than other biomarkers used for diagnosis of these conditions, such as procalcitonin (0.589), C-reactive protein (0.705), or neutrophils (0.605).

Conclusions: Gene expression patterns provided a robust tool to distinguish septic shock from non-septic shock postsurgical patients and shows the potential to provide an immediate and specific treatment, avoiding the unnecessary use of broad-spectrum antibiotics and the development of antimicrobial resistance, secondary infections and increase health care costs.

Keywords: Biomarkers; Diagnosis; Gene expression profiling; Sepsis; Shock.