Evaluation of the added value of viral genomic information for predicting severity of influenza infection

Abstract

Abstract Background The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. Methods 160 A(H3N2) influenza positive samples from the 2016–2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. Results The model including viral mutations in addition to the host characteristics had an improved fit ( $${X}^{2}$$ X 2 =12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. Conclusions Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

Keywords

• Seasonal influenza
• Pathogen genomics
• High-dimensional data analysis
• Predictive modeling