Patients with sepsis need to be differentiated from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing.

Scientists working for Athlomics Pty Ltd., (Toowong, QLD, Australia) in collaboration with various local hospitals conducted a multicenter, prospective clinical trial across four tertiary critical care settings in Australia. Twenty-seven sepsis patients were recruited provided they had clinical evidence of systemic infection based on microbiology diagnoses. A participant group of 38 was postsurgical (PS), was recruited preoperatively, and their blood samples collected within 24 hours following surgery. There was also a healthy control (HC) group of 20 participants.

Each participant had minimally 5 mL of PAXgene (PreAnalytix; Hombrechtikon, Switzerland) blood collected for leucocyte ribonucleic acid (RNA) isolation and gene expression analyses. Studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori using the Affymetrix array (Santa Clara, CA, USA) and multiplex tandem-polymerase chain reaction (MT-PCR) assays. An algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes.

Based on preliminary microarray analyses comparing sepsis groups and HC, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, leucocyte differentiation, extracellular remodeling, and immune modulation pathways. Comparisons against Gene Expression Omnibus (GEO) data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. The area under the curve (AUC) receiver operator characteristics (ROC) curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86% and 92%.

The authors concluded that this novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. These findings suggest that this sepsis gene-expression test is robust and ongoing studies will determine whether it performs equally well when applied to different patient populations and ethnic groups. The study was first published online on June 20, 2011, in the journal Critical Care.

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