







Item type: | Article | ||||
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Journal or Publication Title: | Clinical Infectious Diseases | ||||
Publisher: | Oxford Univ. Press | ||||
Place of Publication: | CARY | ||||
Volume: | 75 | ||||
Number of Issue or Book Chapter: | 1 | ||||
Page Range: | e1063-e1071 | ||||
Date: | 2021 | ||||
Institutions: | Medicine > Lehrstuhl für Innere Medizin III (Hämatologie und Internistische Onkologie) Medicine > Lehrstuhl für Medizinische Mikrobiologie und Hygiene Medicine > Lehrstuhl für Neurologie Medicine > Abteilung für Krankenhaushygiene und Infektiologie | ||||
Identification Number: |
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Keywords: | SARS-CoV-2; COVID-19; microbiome; dysbiosis; machine learning | ||||
Dewey Decimal Classification: | 600 Technology > 610 Medical sciences Medicine | ||||
Status: | Published | ||||
Refereed: | Yes, this version has been refereed | ||||
Created at the University of Regensburg: | Partially | ||||
Item ID: | 57286 |
Abstract
Background At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict ...

Abstract
Background
At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (COVID-19) illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders. Methods
To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multicenter, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate, and severe COVID-19 (n = 322 participants).
Results
In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features. Conclusions
In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.
Metadata last modified: 08 Jan 2025 07:20