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

Content Provider	Springer Nature : BioMed Central
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
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.
Related Links	https://ccforum.biomedcentral.com/counter/pdf/10.1186/cc10274.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	13648535
DOI	10.1186/cc10274
Journal	Critical Care
Issue Number	3
Volume Number	15
Language	English
Publisher	BioMed Central
Publisher Date	2011-06-20
Access Restriction	Open
Subject Keyword	Intensive Critical Care Medicine Emergency Medicine Systemic Inflammatory Response Syndrome Receiver Operator Characteristic Receiver Operator Characteristic Curve Sepsis Patient Gene Expression Omnibus Database
Content Type	Text
Resource Type	Article
Subject	Critical Care and Intensive Care Medicine
Journal Impact Factor	8.8/2023
5-Year Journal Impact Factor	10.4/2023

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