Background Expression of selected cytokines in children with respiratory syncytial virus (RSV) infections has shown some association with severity of symptoms, yet these associations are not always strong or reproducible.
Objective Analyse whether distinct cytokine “patterns”, rather than single cytokines, can discriminate (and thus predict) infants with different severity of RSV-induced respiratory illness.
Methods 186 children ⩽2 years of age with symptoms of respiratory infections were enrolled. Nasopharyngeal secretions were analysed for viral pathogens and 27 cytokines by bead-based multiplex panel. Children were assigned a clinical diagnosis of upper respiratory infection (URI) alone or bronchiolitis, with or without hypoxia. Analysis of variance and Kruskal–Wallis tests were used to compare cytokine concentrations between groups and hierarchical clustering to reveal natural patterns in cytokine data. Supervised analysis (classification using decision trees and regression trees) was used to identify features that best predicted disease severity.
Results 71 children diagnosed with RSV infections clustered into three major groups with low and high cytokines. A decision tree classifier based on IL-17 and macrophage inflammatory protein 1beta was able to discriminate URI from bronchiolitis (81% accuracy) and one based on IL-17 alone to classify children with bronchiolitis without or with hypoxia (80% accuracy). A logistic regression analysis that included IL-17 was equally accurate in discriminating bronchiolitis cases without hypoxia from those with hypoxia.
Conclusions Our study provides proof of concept that informative patterns of cytokines can be detected and interpreted in nasopharyngeal secretions from patients with respiratory viral infection and may contribute to more objective classification of disease severity/type.
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