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

Sutherland, Allison, Thomas, Mervyn, Brandon, Roslyn A., Brandon, Richard B., Lipman, Jeffrey, Tang, Benjamin, McLean, Anthony, Pascoe, Ranald, Price, Gareth, Nguyen, Thu, Stone, Glenn and Venter, Deon (2011) Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Critical Care, 15 3: 1-11. doi:10.1186/cc10274


Author Sutherland, Allison
Thomas, Mervyn
Brandon, Roslyn A.
Brandon, Richard B.
Lipman, Jeffrey
Tang, Benjamin
McLean, Anthony
Pascoe, Ranald
Price, Gareth
Nguyen, Thu
Stone, Glenn
Venter, Deon
Title Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis
Journal name Critical Care   Check publisher's open access policy
ISSN 1364-8535
Publication date 2011-06
Sub-type Article (original research)
DOI 10.1186/cc10274
Open Access Status DOI
Volume 15
Issue 3
Start page 1
End page 11
Total pages 11
Place of publication London, England, U.K.
Publisher BioMed Central Ltd.
Collection year 2012
Language eng
Formatted abstract
Introduction
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.

Methods
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.

Results
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%.

Conclusions
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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number R149

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Medicine Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 45 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 50 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 22 Sep 2011, 10:45:15 EST by Matthew Lamb on behalf of School of Medicine