(1452) Analysis of geNe Expression and bioMarkers fOr poiNt-of-care dEcision support in Sepsis (ANEMONES)

Introduction: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. Here, we describe identification of transcriptional mRNA biomarkers from peripheral blood leukocytes (PBL), able to identify severe inflammation and differentiate Sepsis from SIRS, in adult patients with critical illness in a multicenter clinical study (ISRCTN99754654).

Methods: We recruited 59 patients with abdominal sepsis, 84 patients with pulmonary sepsis, 42 patients with out-of-hospital cardiac arrest (SIRS) in addition to 30 healthy control donors. Blood samples were collected from sepsis and SIRS patients at Day 1, Day 2 and Day 5 of admittance to ICU and on discharge, and on Day 1 from healthy volunteers. RNA was prepared from patient PBLs. Data was analysed with standard parametric methods and using a stepwise Artificial Neural Network approach incorporating Monte Carlo cross validation and a supervised learning approach.

Results: The groups were well matched for baseline characteristics, including severity and organ dysfunction. 19 select high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p< 0.05), termed ‘indicators of inflammation’ (IoI), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty select ‬entities were differentially expressed between sepsis and SIRS (FC>2.0, p-value < 0.05), termed ‘SIRS or Sepsis’ (SoS) biomarkers. The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758).

Conclusions: We have discovered parsimonious sets of genes which in a two-tier model can differentiate between healthy controls and individuals with systemic inflammation with very high accuracy and are then able to discriminate between sepsis and SIRS. Firstly, the IoI signature can be utilized to identify Systemic Inflammation, followed by stratification of Sepsis from SIRS using the SoS Signature. Both signatures consist of 5 differentially expressed biomarkers, some of which many have been previously identified as sepsis-associated.