COVID-19 patient variables associated with the detection of airborne SARS-CoV-2

Background: Understanding the COVID-19 patient characteristics that impact environmental SARS-CoV-2 load is essential for improving infection risk management. In this study, we analyzed the influence of patient variables on airborne SARS-CoV-2 genome detection. Methods: Sixty-nine COVID-19 patients...

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Detalles Bibliográficos
Autores: Truyols-Vives, Joan, Escarrer-Garau, Gabriel, Arbona-González, Laura, Toledo-Pons, Núria, Sauleda Roig, Jaume, Ferrer, Miguel David, Fraile-Ribot, Pablo, Doménech-Sánchez, Antonio, García-Baldoví, Herme, Sala-Llinàs, Ernest, Colom-Fernández, Antoni, Mercader-Barceló, Josep
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Conselleria de Salut i Consum del Govern de les Illes Balears
Repositorio:Docusalut
Idioma:inglés
OAI Identifier:oai:docusalut.com:20.500.13003/25101
Acceso en línea:https://hdl.handle.net/20.500.13003/25101
Access Level:acceso abierto
Palabra clave:Airborne transmission
COVID-19
Droplet digital PCR
Preventive measures
SARS-CoV-2
Descripción
Sumario:Background: Understanding the COVID-19 patient characteristics that impact environmental SARS-CoV-2 load is essential for improving infection risk management. In this study, we analyzed the influence of patient variables on airborne SARS-CoV-2 genome detection. Methods: Sixty-nine COVID-19 patients were recruited across three independent studies with airborne SARS-CoV-2 genome assessed in individual hospital rooms using droplet digital PCR. Results: In the bivariate analysis, the odds of airborne SARS-CoV-2 detection were significantly higher for patients with obesity, chronic respiratory diseases, pneumonia at admission, sampling, and discharge, and lower lymphocytes count. No significant associations were found between airborne SARS-CoV-2 detection and symptoms presence or duration, nor with the results of the most recent positive nasopharyngeal PCR test prior to air sampling. In the multivariate analysis, the best-fit model included patient age, type of admission, and symptoms duration. Patient age significantly contributed to the risk of airborne SARS-CoV-2 detection in the multivariate analysis. Conclusions: Our findings highlight the variability in individual responses to SARS-CoV-2 infection and suggest that factors linked to COVID-19 severity, symptomatology, and immunocompetence influence the airborne SARS-CoV-2 detection. Our results may support the development of more precise preventive measures in healthcare settings.