Fig 1.
Gene expression data from 48 bacterial (B), 54 viral (V), and 49 noninfectious (N) microbiologically confirmed cases were used to train classifiers capable of distinguishing between bacterial, viral, and noninfectious causes of illness. Specifically, the model created 3 separate classifiers: bacterial versus non-bacterial, viral versus non-viral, and infectious versus non-infectious. The fixed-weight classifiers were then applied to a cohort of patients with acute asthma exacerbations of various etiologies to predict the underlying trigger of the exacerbation.
Table 1.
Demographic information for the training and asthma exacerbation validation cohorts.
Table 2.
Clinical data from asthma exacerbation cohorts.
Fig 2.
Symptom scores across clinically adjudicated phenotypes.
Illness duration and symptom scores from each subject were compared between the different clinically adjudicated phenotypes. Symptom scores were graded on a 0 to 4 scale, with 0 indicating not present and 4 indicating “very severe”. Nasal symptoms represented an average of nasal discharge, nasal stuffiness, and sneezing. Overall symptom severity was simply the sum of all symptom scores. Most symptoms were similar between groups. Only two reached statistical significance: fever/chills (p = 0.005) and a composite of overall symptom severity (p = 0.02). Kruskal-Wallis statistical test was used to assess significance.
Fig 3.
Gene expression classification of the asthma exacerbation cohort.
The fixed-weight gene expression model developed in the training cohort was applied to each of the 46 asthma exacerbation cases to determine gene expression-based classification. Clinical adjudication groups were subsequently stratified by gene expression class, with the respective percentages indicated at the top of each bar. Viral and noninfectious groups had a high degree of agreement between adjudication and model classification (83.3% and 61.9%, respectively), but those adjudicated bacterial were often classified as viral or noninfectious by the model.
Fig 4.
Dimensionality reduction to visualize differences between asthma exacerbation subgroups with respect to all gene targets.
Unsupervised analysis using t-distributed stochastic neighbor embedding (TSNE) was utilized to depict the visual relationships between all 46 subjects with asthma exacerbations. Each subject is labeled by their respective bacterial, viral, or noninfectious status, based on clinical adjudication. Notably, in TSNE, the axes are not meant to be interpretable; this plot represents a 3D projection of higher dimensional space.
Fig 5.
Frequency of antibiotic prescription in different infection classes.
Antibiotic prescription during emergency room or hospital stay was recorded for each subject. Antibiotic prescription rate was calculated for the clinical adjudication groups and this was compared to the antibiotic prescription rate in the model-predicted groups. The respective percentages are indicated at the top of each bar. Antibiotics were prescribed more frequently for subjects with viral and noninfectious signatures than for subjects with bacterial signatures.