ArticleBMJ open2026
Association of influenza viral genetic information with severity markers in patients hospitalised with influenza: multicentre retrospective cohort study.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
41 authors.
Funding
Abstract
objectiveThe objective of this study was to determine the association between viral subtype/clade and disease severity.
designMulticentre retrospective cohort study.
settingThis study used data from the Global Influenza Hospital Surveillance Network (GIHSN). The dataset comprised hospitalised influenza patients with viral sequencing data across 14 countries, collected from August 2022 through October 2023.
participantsA total of 761 hospitalised patients were enrolled during the study period, and 745 patients were included in the analysis. We excluded patients with missing data on explanatory or outcome variables, those infected with viral clades represented by fewer than 11 sequences, and those enrolled at study sites contributing fewer than 5 patients. OUTCOME MEASURES: Disease severity was defined by admission to intensive care unit (ICU), receipt of non-invasive oxygen supplementation, 3-variable definition (ICU, mechanical ventilation or death) or 4-variable definition (3-variable plus oxygen supplementation).Outcomes were analysed in association with subtype or clade using the mixed-effects logistic regression models, adjusting for age group, sex, underlying medical conditions, influenza vaccination status, antiviral use, country income level and epidemic period, while study site was included as a random effect.
results745 patients were included: 263 A(H1N1)pdm09, 380 A(H3N2), 102 B/Victoria. A(H1N1)pdm09 infection was associated with increased odds of ICU admission (adjusted ORs (aORs) 2.5, 95% CI 1.1 to 5.8) compared with A(H3N2). 6B.1A.5a.2a.1 clade of A(H1N1)pdm09 was associated with increased severity compared with 6B.1A.5a.2a clade (aOR 3.0, 95% CI 1.0 to 9.5) and (aOR 5.4, 95% CI 1.6 to 18.3) for the 3-variable and 4-variable definitions respectively. Among A(H3N2), the (3C.2a1b.2a.)2b clade showed a trend toward increased severity using the 4-variable definition compared with the 2a.1b clade (aOR 2.9, 95% CI 0.8 to 10.0).
conclusionsThis analysis highlights the differential impact of influenza subtypes and clades on disease severity in hospitalised patients. Future research should investigate the role of specific viral mutations of these clades in modulating immune evasion or disease severity. These findings reinforce the GIHSN's critical role in global surveillance. Ongoing genomic surveillance is crucial for understanding the clinical impact of emerging influenza variants and informing public health responses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.