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

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.

Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock.

Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.