SeptiCyte LAB appears to be a promising diagnostic tool to complement physician assessment of infection likelihood in critically ill adult patients with systemic inflammation. Clinical trial registered with www.clinicaltrials.gov (NCT01905033 and NCT02127502).

The innate immune system of humans and other mammals responds to pathogen-associated molecular patterns (PAMPs) that are conserved across broad classes of infectious agents such as bacteria and viruses. We hypothesized that a blood-based transcriptional signature could be discovered indicating a host systemic response to viral infection. Previous work identified host transcriptional signatures to individual viruses including influenza, respiratory syncytial virus and dengue, but the generality of these signatures across all viral infection types has not been established. Based on 44 publicly available datasets and two clinical studies of our own design, we discovered and validated a four-gene expression signature in whole blood, indicative of a general host systemic response to many types of viral infection. The signature’s genes are: Interferon Stimulated Gene 15 (ISG15), Interleukin 16 (IL16), 2′,5′-Oligoadenylate Synthetase Like (OASL), and Adhesion G Protein Coupled Receptor E5 (ADGRE5). In each of 13 validation datasets encompassing human, macaque, chimpanzee, pig, mouse, rat and all seven Baltimore virus classification groups, the signature provides statistically significant (p < 0.05) discrimination between viral and non-viral conditions. The signature may have clinical utility for differentiating host systemic inflammation (SI) due to viral versus bacterial or non-infectious causes.

SeptiCyte Lab is able to discriminate clearly between clinically well-defined and homogeneous postcardiopulmonary bypass and clinically overt severe sepsis syndrome groups in children. A broader investigation among children with more heterogeneous inflammation-associated diagnoses and care settings is warranted.

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Background There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions. Methods We used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses. Results The combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90–1), compared to 0.79 (0.68–0.91) for WCC and 0.73 (0.61–0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90–0.99) and 0.86 (0.64–0.96), compared to 0.90 (0.80–0.94) and 0.66 (0.48–0.79) for WCC and 0.88 (0.69–0.95) and 0.60 (0.31–0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials. Conclusions Blood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections.