Date Published: April 8, 2019
Publisher: Public Library of Science
Author(s): Emily C. Lydon, Charles Bullard, Mert Aydin, Olga M. Better, Anna Mazur, Bradly P. Nicholson, Emily R. Ko, Micah T. McClain, Geoffrey S. Ginsburg, Chris W. Woods, Thomas W. Burke, Ricardo Henao, Ephraim L. Tsalik, Aran Singanayagam.
Asthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse.
To explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations.
Subjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin.
Of 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics.
Our model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.
Asthma exacerbations are responsible for an estimated 1.7 million emergency department visits annually in the United States. Exacerbations are frequently due to extrinsic causes such as bacterial infections, viral infections, or noninfectious causes, such as medication noncompliance or environmental exposure[2, 3]. Antibiotics are not standard of care for asthma exacerbations, and several trials have shown no benefit to the use of macrolides for asthma exacerbations[4–7]. Despite this, 22% of patients presenting to the emergency room and 58% of patients hospitalized for asthma receive antibiotics, leading to antibiotic resistance, antibiotic-related adverse events, and additional costs[8, 9]. Antibacterials and antivirals are only effective in those with confirmed bacterial and influenza infections, respectively, but it is often difficult to identify these etiologies at the point of care[10, 11].
In this study, we applied a host response signature to subjects presenting with asthma exacerbations and classified them by their underlying bacterial, viral, or noninfectious gene expression patterns. We compared this approach to clinical adjudication and procalcitonin. Our results indicated that peripheral blood gene expression profiles may provide additional clinically useful information for distinguishing the major classes of asthma exacerbation triggers. In the acute care setting, such a task is challenging since asthma exacerbations often have similar presentations and existing diagnostics fail to identify an underlying cause in most. As a result, antibiotics are overprescribed—nearly half of the individuals in this study were given antibiotics, despite only a handful being adjudicated as having bacterial infection. This impacts antibiotic stewardship efforts as well as patient outcomes; a recent study reported longer admissions and higher cost of stay in asthmatics treated with antibiotics. Gene expression approaches, once translated onto a clinically useful platform, could help abate empiric prescribing practices, mitigate antibiotic resistance, and improve patient care.