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EBioMedicine. 2019 Oct;48:453-461. doi: 10.1016/j.ebiom.2019.09.040. Epub 2019 Oct 17.

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

Author information

1
Duke University School of Medicine, Durham, NC, USA; Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA.
2
Duke University Department of Biostatistics and Informatics, Durham, NC, USA.
3
Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA.
4
Institute of Medical Research, Durham Veterans Affairs Medical Center, Durham, NC, USA.
5
University of North Carolina Medical Center, Chapel Hill, NC, USA.
6
Duke University Department of Medicine, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA.
7
University of North Carolina Medical Center, Chapel Hill, NC, USA; United Arab Emirates University, Al Ain, UAE.
8
Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
9
Henry Ford Hospital System, Detroit, MI, USA.
10
University of South Alabama Health University Hospital, Mobile, AL, USA.
11
Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Department of Hospital Medicine, Duke Regional Hospital, Durham, NC 27705, USA.
12
Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA.
13
Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA. Electronic address: chris.woods@duke.edu.
14
Duke University Center for Applied Genomics and Precision Medicine, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA. Electronic address: e.t@duke.edu.

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

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