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Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis

Allison Sutherland1*, Mervyn Thomas1, Roslyn A Brandon1, Richard B Brandon1, Jeffrey Lipman23, Benjamin Tang4, Anthony McLean4, Ranald Pascoe5, Gareth Price6, Thu Nguyen6, Glenn Stone1 and Deon Venter6*

Author Affiliations

1 Division of Immunobiology and Bioinformatics, Athlomics Pty Ltd, Jephson Street, Toowong, QLD 4066, Australia

2 Burns Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Queensland, QLD 4072, Australia

3 Department of Intensive Care Medicine, Royal Brisbane & Women's Hospital, Butterfield Street, Herston, QLD 4029, Australia

4 Department of Intensive Care Medicine, Nepean Hospital, Western Clinical School, University of Sydney, Derby Street, Kingswood, NSW 2747, Australia

5 Department of Intensive Care Medicine, Wesley Hospital, Coronation Drive, Auchenflower, QLD 4066, Australia

6 Department of Pathology, Mater Health Services, Raymond Terrace, South Brisbane, QLD 4101, Australia

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Critical Care 2011, 15:R149  doi:10.1186/cc10274

Published: 20 June 2011



Sepsis is a complex immunological response to infection characterized by early hyper-inflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on Systemic Inflammatory Response Syndrome (SIRS) differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing.


This was a multi-centre, prospective clinical trial conducted across four tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n = 27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n = 38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n = 20). Each participant had minimally 5 ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR 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. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes.


Based on preliminary microarray analyses comparing HC and sepsis groups, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against 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%.


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.