Validation of a host response test to distinguish bacterial and viral respiratory infection

Abstract

Background: Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases.

Methods: Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin.

Findings: 151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85-0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02).

Interpretation: The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.

Keywords: Biomarkers; Coinfection; Diagnosis; Gene expression; Precision medicine; Respiratory tract infections.

Conflict of interest statement

TWB reports other from Predigen Inc, outside the submitted work; In addition, TWB has a patent “METHODS TO DIAGNOSE AND TREAT ACUTE RESPIRATORY INFECTIONS” (USPTO Pub. No.: US 2018/0245154 A1) pending. CBC reports grants from National Institutes of Health, grants from DARPA, during the conduct of the study; personal fees from National Institutes of Health, outside the submitted work. VGF served as Chair of the V710 Scientific Advisory Committee (Merck); has received grant support from Cerexa/Actavis/Allergan, Pfizer, Advanced Liquid Logics, NIH, MedImmune, Basilea Pharmaceutica, Karius, ContraFect, Regeneron Pharmaceuticals, and Genentech; has NIH STTR/SBIR grants pending with Affinergy, Locus, and Medical Surface, Inc; has been a consultant for Achaogen, AmpliPhi Biosciences, Astellas Pharma, Arsanis, Affinergy, Basilea Pharmaceutica, Bayer, Cerexa Inc., ContraFect, Cubist, Debiopharm, Durata Therapeutics, Grifols, Genentech, MedImmune, Merck, The Medicines Company, Pfizer, Novartis, NovaDigm Therapeutics Inc., Theravance Biopharma, Inc., XBiotech, and has received honoraria from Theravance Biopharma, Inc., and Green Cross, and has a patent pending in sepsis diagnostics. GSG reports other from Predigen outside the submitted work; In addition, GSG has a patent “Biomarkers for the molecular classification of bacterial infection” US 14/1880,668 pending, and a patent “Methods of Identifying Infectious Disease and Assays for Identifying Infectious Disease” US patent US 8,821,876 issued. RH reports grants from ARLG/NIH during the conduct of the study; In addition, RH has a patent “Methods to Diagnose and Treat Acute Respiratory Infections” (Application #PCT/US2016/040437) pending. SFK reports grants from National Institutes of Health during the conduct of the study. MTM reports consultant fees from UpToDate, outside the submitted work; In addition, MTM has a patent Patent pending on Genomic Diagnostics for Respiratory Infections. EBQ reports grants from NIAID, grants from DARPA during the conduct of the study. ELT reports personal fees from Duke University, personal fees from Durham VA Health Care System, grants from DARPA, grants from NIH/ARLG, other from Predigen, Inc., grants from NIH/VTEU, personal fees from bioMerieux during the conduct of the study. In addition, DELT has a patent “Biomarkers for the molecular classification of bacterial infection” pending, and a patent “Methods to diagnose and treat acute respiratory infections” pending. CWW reports personal fees from Duke University, personal fees from Durham VA Health Care System, grants from DARPA, grants from NIH/ARLG, other from Predigen, Inc., grants from NIH/VTEU, personal fees from bioMerieux, personal fees from IDbyDNA, personal fees from Giner, grants from BioFire, grants from Janus, grants from BioMeme, grants from RTI, personal fees from Roche Molecular Sciences, during the conduct of the study; personal fees from Becton Dickinson, grants from Pfizer, grants from Openbiome, grants from MRI Global, personal fees from Sanofi, outside the submitted work; In addition, CWW has a patent “Biomarkers for the molecular classification of bacterial infection “pending, and a patent “ Methods to diagnose and treat acute respiratory infections pending”. Other authors not explicitly listed above have nothing to disclose.

Copyright © 2019. Published by Elsevier B.V.

Figures

Fig. 1

Experimental flow. Coinfection cases included both superinfections (acute bacterial infection following an acute viral infection) and acute-on-chronic coinfections (acute bacterial infection and chronic viral infection). Suspected bacterial cases were those without microbiological evidence but clinically adjudicated as bacterial. RT-PCR: Real Time Polymerase Chain Reaction; AoC: acute-on-chronic.

Fig. 2

RT-PCR test performance compared to procalcitonin for microbiologically confirmed, single etiology cases. Upper panels demonstrate AUROC curves for the bacterial, viral, and noninfectious classifiers. Lower panels show the bacterial, viral, and non-infectious probabilities for each subject, organized by the clinically adjudicated phenotype. Procalcitonin comparison is shown on the right side of the panel (values are in ng/mL). A maximum procalcitonin value of 10 ng/mL was used to improve data visualization. RT-PCR: Real time polymerase chain reaction; AUROC: area under receiver operator characteristic; NI: non-infectious illness.

Fig. 3

Signature application in cases of superinfection. “Superinfection” describes subjects with an acute bacterial infection temporally following an acute viral infection. The red and black lines (left and right, respectively) depict the thresholds for bacterial infection and viral infection, respectively. The dashed lines divide the subjects into their model-predicted classes based on thresholding: bacterial infection, viral infection, coinfection, and no infection. 3A, Model application in microbiologically confirmed superinfections (n = 19). 3B, Model application in clinically adjudicated superinfections without microbiological confirmation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Signature application in cases of acute-on-chronic coinfections. “Acute-on-chronic” coinfection describes subjects with chronic viral infection and acute bacterial infection. All subjects had microbiologically confirmed acute bacterial infections. The red and black lines (left and right, respectively) show the thresholds for bacterial infection and viral infection, respectively. The dashed lines divide the subjects into their model-predicted classes based on thresholding: bacterial infection, viral infection, coinfection, and no infection. 4A, Model application in chronically infected subjects with detectable or unknown viral load (n = 8). 3B, Model application in chronically infected subjects with a suppressed viral load (n = 4). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Signature application in cases of suspected bacterial infections. “Suspected bacterial” describes subjects clinically adjudicated as bacterial infection but without microbiological confirmation (n = 39). The red and black lines (left and right, respectively) show the thresholds for bacterial infection and viral infection, respectively. The dashed lines divide the subjects into their model-predicted classes based on thresholding: bacterial infection, viral infection, coinfection, and no infection. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)