Blood transcriptomic discrimination of bacterial and viral infections in the emergency department: A multi-cohort observational validation study

Abstract

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.