Bibliographic Details
| Title: |
Evaluation of the added value of viral genomic information for predicting severity of influenza infection |
| Authors: |
Nina Van Goethem, Annie Robert, Nathalie Bossuyt, Laura A. E. Van Poelvoorde, Sophie Quoilin, Sigrid C. J. De Keersmaecker, Brecht Devleesschauwer, Isabelle Thomas, Kevin Vanneste, Nancy H. C. Roosens, Herman Van Oyen |
| Source: |
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-14 (2021) |
| Publisher Information: |
BMC, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Infectious and parasitic diseases |
| Subject Terms: |
Seasonal influenza, Pathogen genomics, High-dimensional data analysis, Predictive modeling, Infectious and parasitic diseases, RC109-216 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2334 |
| Relation: |
https://doaj.org/toc/1471-2334 |
| DOI: |
10.1186/s12879-021-06510-z |
| Access URL: |
https://doaj.org/article/9bbb548592214532a9f69a55d6f35339 |
| Accession Number: |
edsdoj.9bbb548592214532a9f69a55d6f35339 |
| Database: |
Directory of Open Access Journals |
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