A two-transcript biomarker of host classifier genes for discrimination of bacterial from viral infection in acute febrile illness: a multicentre discovery and validation study

Abstract

Background Acute febrile illness is one of the main reasons for outpatient hospital visits worldwide. However, differential diagnosis between bacterial and viral causes is challenging and misdiagnosis can result in antimicrobial overuse and hinder prompt treatment. We aimed to build and validate a diagnostic model to discriminate bacterial from viral infection in acute febrile illness by evaluating the expression of potential classifier host genes.Methods In this multicentre discovery and validation study, we included patients aged 14-85 years with acute febrile illness (fever for ≤14 days, axillary temperature of ≥38°C, and confirmed bacterial infection, viral infection, or noninfectious inflammatory disease), and healthy control participants (no significant medical history and no fever within the past 90 days) from four hospitals in Shandong province, China. Patients from the first hospital were divided into the screening, discovery, and internal validation groups, and patients from the three other hospitals comprised the external validation group. We measured expression of candidate genes in peripheral blood by RT-PCR, and patients for whom a successful RT-PCT result was recorded were included in the next-step analysis. For patients from the first hospital, those enrolled during the early phase of the study were assigned to the screening group, which was used to identify the optimal transcripts (IFI44L and PI3) for discrimination between bacterial and viral infections by screening four candidate genes (FAM89A, IFI44L, PI3, and ITGB2) by RT-PCR. The remaining patients were then randomly assigned (1:1) to discovery and internal validation groups by time of admission and blood drawing via the equidistant random sampling method. A logistic regression model integrating the mRNA levels of IFI44L and PI3 was built by use of the discovery group, and the diagnostic performance of the model was evaluated in the internal and external validation groups using area under the receiver operating curve (AUC), sensitivity, and specificity.

Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness

“…Further information available in the Supplement. Article (reference) Study Design Study setting Age range Biomarker Measure of effect Test platform Control group Barry 2018 49 Case control Ningxia Hui Autonomous region in north-western China 18 to 91 years old 5-miRNA signature: miRs −29a, −99b, −21, −146a, −652 Sens 0.94 (95%CI 0.87-0.98) Spec 0.88 (95%CI 0.80-0.94) qRT-PCR Healthy adults Mahle (2021) 50 Case control Emergency Departments of Duke University Medical Center, Durham VA Health Care System, UNC Health Care, and Henry Ford Hospital 14 to 94 years old 81-gene signature Sens 0.80 (95%CI 0.72-0.88) Spec 0.80 (95%CI 0.74-0.86) RT-PCR Viral infection and non-infectious illness Mendelsohn (2021) 51 Cross sectional Five communities in South Africa with a high TB burden 28 to 42 years RISK11 signature Sens 0.88 (95%CI 0.58-1.0) Spec 0.66 (95%CI 0.63-0.69) RT-PCR N/A Xu (2021) 52 Case control Four hospitals in Shandong province, China 17 to 85 years 2-transcript biomarker (IFI44L and PI3 transcripts) Sens 0.86 (95%CI 0.71-0.94) Spec 0.95 (95%CI 0.85-0.99) RT-PCR Viral infection; SLE Francisco 2017 53 Case control China 18 to 84 years GBP5 , DUSP3 , KLF2 Sens 0.76 (95%CI 0.71-0.81) Spec 0.86 (95%CI 0.81-0.90) RT-PCR Healthy adults Pan 2019 54 Case control Beijing Chest Hospital, Beijing Chao-yang Hospital, Beijing Tiantan Hospital, Beijing Ditan Hospital, Xuanwu Hospital and People's Liberation Army 263 hospital 18 to 80 years 4 miRNA panel (miR-126-3p, miR-130a-3p, miR-151a-3p, and miR-199a-5p) Sens 0.82 (95%CI 0.48-0.98) Spec 0.90 (0.55-1.0) Microarray Viral meningitis …”

Cytokine Storm—Definition, Causes, and Implications

“…Because this viral signature relies on only four biomarkers, it could potentially become clinically useful when implemented on a point-of-care platform. Recently, Xu and colleagues presented a two-transcript biomarker with the RT-PCR transcripts of the host genes IFI44L and PI3 as a robust classifier to discriminate bacterial infections from viral infections in adults with acute febrile illness [ 224 ].…”

Host gene signature shows promise to distinguish bacterial and viral infections

“…In The Lancet Digital Health , Nannan Xu and colleagues 7 present a novel exploration of four host genes in patients with acute febrile illness using real-time PCR. Building on the past decade of research into host-immune response signatures, the investigators used existing gene microarray and RNA-sequencing datasets to shortlist candidate gene biomarkers.…”

“…A particular challenge for the use of biomarkers in acute febrile illness, particularly C-reactive protein, is a lag-time between symptom onset and measurable biomarker changes. 8 Subgroup analyses by Xu and colleagues 7 suggested their signature might be sensitive in early infection, with one patient identified as bacterial within 2 h of symptom onset. This early change is one potential explanation for the high performance compared with traditional biomarkers.…”

“…Xu and colleagues 7 comment that trading some specificity for higher sensitivity might be needed to catch more bacterial infections, particularly in severely unwell patients. Diagnostic tests are never used in isolation, and the entire clinical picture is required to make safe decisions.…”