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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.

SUBMITTER: Sampson D

PROVIDER: S-EPMC7372897 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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

Sampson Dayle D Yager Thomas D TD Fox Brian B Shallcross Laura L McHugh Leo L Seldon Therese T Rapisarda Antony A Hendriks Roslyn A RA Brandon Richard B RB Navalkar Krupa K Simpson Nandi N Stafford Sian S Gil Eliza E Venturini Cristina C Tsaliki Evi E Roe Jennifer J Chain Benjamin B Noursadeghi Mahdad M

BMC medicine 20200721 1

<h4>Background</h4>There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions.<h4>Methods</h4>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 ...[more]

PMID: 32690014

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Project description:BackgroundDifferentiating infants with adverse events following immunisation (AEFIs) or invasive bacterial infection (IBI) is a significant clinical challenge. Young infants post vaccination are therefore often admitted to the hospital for parenteral antibiotics to avoid missing rare cases of IBI.MethodsDuring a service evaluation project, we conducted a single-centre retrospective observational study of infants with IBI, urinary tract infection (UTI) or AEFI from two previously published cohorts. All patients presented to hospital in Oxfordshire, UK, between 2011 and 2018, spanning the introduction of the capsular group-B meningococcal vaccine (4CMenB) into routine immunisation schedules. Data collection from paper and electronic notes were unblinded. Clinical features, including National Institute for Health and Care Excellence (NICE) 'traffic light' risk of severe illness and laboratory tests performed on presentation, were described, and comparisons made using regression models, adjusting for age and sex. We also compared biochemical results on presentation to those of well infants post vaccination, with and without 4CMenB regimens.ResultsThe study included 232 infants: 40 with IBI, 97 with probable AEFI, 24 with possible AEFI, 27 with UTI and 44 post vaccination 'well' infants. C-reactive protein (CRP) was the only discriminatory blood marker, with CRP values above 83 mg/L only observed in infants with IBI or UTI. NICE risk stratification was significantly different between groups but still missed cases of IBI, and classification as intermediate risk was non-differential. Fever was more common in probable AEFI cases, while seizures and rashes were equally frequent. Diarrhoea and clinician-reported irritability or rigours were all more common in IBI.ConclusionsClinical features on presentation may aid risk stratification but cannot reliably differentiate IBI from AEFI in infants presenting to the emergency department. Blood results are generally unhelpful due to post vaccination inflammatory responses, particularly in children receiving 4CMenB vaccination. Improved biomarkers and clinical prediction tools are required to aid management in febrile infants post vaccination.

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Dataset Information

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

Publications

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

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