Host gene expression classifiers diagnose acute respiratory illness etiology

Abstract

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational, cohort study determined whether host gene expression patterns discriminate non-infectious from infectious illness, and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or non-infectious illness as well as 44 healthy controls was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a non-infectious cause of illness (26 probes). Overall accuracy was 87% (238/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p<0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). A sixth publically available dataset included twenty-five patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI; viral ARI; co-infection; and neither a bacterial nor viral response. These findings create an opportunity to develop and utilize host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

Host-Based Peripheral Blood Gene Expression Analysis for Diagnosis of Infectious Diseases

“…These findings were further confirmed in an adult cohort of hospitalized patients, where gene expression-based classifiers were shown to be superior to a single-analyte biomarker, procalcitonin (sensitivity of 95% versus 38% and specificity of 92% versus 91%, respectively) (19). More recent work led to the development of an RT-PCR-based classifier that accurately separates acute upper respiratory illness into bacterial infection, viral infection, bacterium-virus coinfection, or noninfectious causes (20).…”

Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition? (Preprint)

“…Diagnostic support is desirable in many different RD [18,19]. As the different RD are highly heterogeneous, the affected patients demonstrate obviously many differences.…”

“…we followed a similar approach as proposed by Tsalik [18], where separate independent classifiers were constructed for pairs of classes or disease groups, instead of one classifier distinguishing between all four groups. Taking also into account that the selected groups of NRO and RD are heterogeneous, the full data set was scaled down to 3 independent subsets from a clinical point of view and under the condition of a nearly equal number of questionnaires in each data set pair (Table 3).…”

New Molecular Diagnostic Approaches to Bacterial Infections and Antibacterial Resistance

“…In 2007, Ramilo et al first described host gene expression signatures discriminating bacterial and viral infection. 26 Since that time, additional studies have focused on adult and pediatric cohorts 27,28 ; discriminated different types of viral infection 29 ; distinguished infection from colonization 30 ; identified pre-symptomatic disease states 31 ; added non-infectious illness as more appropriate controls 28 ; and showed superiority to existing biomarkers (i.e., PCT) 28,32 . As important as each of these (and many more) studies have been, they all occurred at the pre-clinical stage of development.…”