Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test

Abstract

Objectives: Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test.

Design: Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results.

Setting: Four U.S. emergency departments.

Patients: Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis.

Interventions: Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes.

Measurements and main results: Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance.

Conclusions: The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.

Trial registration: ClinicalTrials.gov NCT00258869.

Conflict of interest statement

Dr. Tsalik received in-kind support from BioFire Diagnostics by way of consumables and test instruments; received funding from Predigen, Inc.. BioFire, Inc. provided in-kind support for test development reagents used in this study. Drs. Tsalik, Henao, McClain, Ginsburg, Burke, and Woods disclosed filing for a patent pertaining to the signatures discussed in this study (WO 2017/004390 A1). Dr. Montgomery was an employee of BioFire Diagnostics, LLC. Dr. Nawrocki disclosed he has shares in BioMérieux. Dr. Lydon was supported by the Eugene A. Stead Scholarship from Duke University School of Medicine and the Infectious Diseases Society of America Medical Scholars Program. Drs. Tsalik, Ginsburg, and Woods disclosed that they are cofounders of Predigen, Inc. Drs. Tsalik, Ko, Petzold, Cairns, Kingsmore, Fowler, Ginsburg, Burke, and Woods received support for article research from the National Institutes of Health (NIH). Drs. Nawrocki and Hemmert received funding from BioFire Diagnostics, LLC.; and disclosed that they are employees of BioFire Diagnostics, LLC. Dr. Cairns is a consultant for BioMérieux, Inc. Dr. Ko’s institution received funding from the Antibiotic Resistance Leadership Group; disclosed the off-label product use of diagnostic tests. Dr. Petzold received support for article research from the Defense Advanced Research Projects Agency (DARPA) (NIH National Institute of Allergy and Infectious Diseases (NIAID) U01AI066569 and UM1AI104681 U.S. DARPA contract—N66001-09-C2082). Dr. Cairns’ institution received funding from the NIH (NIAID) and the DARPA; and received funding from BioMérieux. Dr. Kingsmore’s institution received funding from the NIH. Dr. Fowler received funding from the NIH, MedImmune, Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Merck; Medical Biosurfaces; Locus; Affinergy; Contrafect; Karius; Genentech, Regeneron, Basilea, and Janssen; received funding from Basilea, Affinergy, Janssen, Basilea, Integrated Biotherapeutics; C3J, Armata, Valanbio; Akagera, Aridis, Novartis, Novadigm, Durata, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., Cerexa, Tetraphase, Trius, MedImmune, Bayer, Theravance, Basilea, Affinergy, Janssen, xBiotech, Contrafect, Regeneron, Destiny, UpToDate; Stock options Valanbio; a patent for sepsis diagnosis (US9850539B2). Dr. McClain disclosed he has patents pending on diagnostic signatures for respiratory infections. Dr. Crisp was an employee of BioFire Diagnostics and is currently an employee of BioMérieux, Inc. Dr. Ginsburg’s institution received funding from DARPA; received support for article research from the Bill & Melinda Gates Foundation. Dr. Burke is a consultant for and holds equity in Predigen, Inc. Dr. Burke’s institution received funding from the NIH; received funding from Predigen, Inc.; disclosed he is a coinventor on patents pending on Molecular Methods to Diagnose and Treat Respiratory Infections. Dr. Hemmert disclosed the off-label product use of BioFire FilmArray System. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Figures

Figure 1:. Experimental Flow.

The Indeterminate Phenotypes group was not used to calculate performance characteristics but used to demonstrate the distribution of HR-B/V test results in these groups. ARI = Acute Respiratory Illness.

Figure 2:. Classification performance.

The test assigns each subject a probability of viral infection (y-axis) and bacterial infection (x-axis). The vertical line is the threshold for bacterial infection (0.275) while the horizontal line is the threshold for viral infection (0.417). The top left region is indicative of viral infection, bottom left indicates no infection, top right suggests bacterial/viral coinfection, and the bottom right indicates bacterial infection. For panels A and B, colors represent the adjudicated phenotype: blue=bacterial, yellow=non-infectious illness, red=viral. A) Classification of 213 training cohort subjects. B) Classification of 209 validation cohort subjects. (C) The probabilities of bacterial infection (y-axis) as measured by the HR-B/V test vs. procalcitonin (x-axis) are plotted for each subject. The vertical line represents a procalcitonin threshold of 0.25ng/mL. The horizontal line is the threshold for a positive bacterial HR-B/V test. Subjects in the training cohort (n=213) are represented by circles whereas validation cohort subjects (n=209) are represented by a plus. Blue represents cases adjudicated as bacterial. Red represents cases adjudicated as non-bacterial (viral or non-infectious illness). The top right region was identified as bacterial by both tests. The bottom left region represents a non-bacterial classification by both tests. (D) Receiver operating characteristic plot for bacterial vs. non-bacterial infection using the HR-B/V test vs. procalcitonin in the training (discovery) and validation cohorts. E) Classification of 36 subjects with bacterial/viral coinfection (superinfection). Red circles represent clinically suspected cases of superinfection without microbiological confirmation. Blue circles represent microbiologically confirmed superinfection. F) 83 suspected viral infections (red circles) and 82 suspected bacterial infections (blue circles) are shown.