153131 20251017144633.0 doi 10.1080/25310429.2025.2477911 sideral 143601 ART-2025-143601 eng Esteban, Patricia Combination of exhaled volatile organic compounds with serum biomarkers predicts respiratory infection severity 2025 Access copy available to the general public Unrestricted Objective. During respiratory infections, host-pathogen interaction alters metabolism, leading to changes in the composition of expired volatile organic compounds (VOCs) and soluble immunomodulators. This study aims to identify VOC and blood biomarker signatures to develop machine learning-based prognostic models capable of distinguishing infections with similar symptoms. Methods. Twenty-one VOCs and fifteen serum biomarkers were quantified in samples from 86 COVID-19 patients, 75 patients with non-COVID-19 respiratory infections, and 72 healthy donors. The populations were categorized into severity subgroups based on their oxygen support requirements. Descriptive and statistical analyses were conducted to assess group differentiation. Additionally, machine learning classifiers were developed to predict disease severity in both COVID-19 and non-COVID-19 patients. Results. VOC and biomarker profiles differed significantly among groups. Random Forest models demonstrated the best performance for severity prediction. The COVID-19 model achieved 93% accuracy, 100% sensitivity, and 89% specificity, identifying IL-6, IL-8, thrombomodulin, and toluene as key severity predictors. In non-COVID-19 patients, the model reached 89% accuracy, 100% sensitivity, and 67% specificity, with CXCL10 and methyl-isobutyl-ketone as key markers. Conclusion.VOCs and serum biomarkers differentiated HD, COVID-19, and non-COVID-19 patients, and enabled the development of high-performance severity prediction models. While promising, these findings require validation in larger independent cohorts. info:eu-repo/grantAgreement/ES/IACS/PT20-00112 info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Letona-Gimenez, Santiago Domingo, Maria Pilar Morte, Elena Universidad de Zaragoza (orcid)0000-0001-9262-2461 Pellejero-Sagastizabal, Galadriel Universidad de Zaragoza Encabo, Maria del Mar (orcid)0000-0003-4891-105X Ramírez-Labrada, Ariel Universidad de Zaragoza (orcid)0000-0002-3888-7036 Sanz-Pamplona, Rebeca Pardo, Julián Universidad de Zaragoza (orcid)0000-0003-0154-0730 Paño, José Ramón Universidad de Zaragoza (orcid)0000-0002-9600-8116 Galvez, Eva M. (orcid)0000-0001-6928-5516 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 1011 566 Universidad de Zaragoza Dpto. Microb.Ped.Radio.Sal.Pú. Área Inmunología 1002 050 Universidad de Zaragoza Dpto. Bioq.Biolog.Mol. Celular Área Biología Celular 31, 1 (2025), [11 pp.] Pulmonolgy Pulmonolgy 2531-0429 3654443 http://zaguan.unizar.es/record/153131/files/texto_completo.pdf Versión publicada 1234446 http://zaguan.unizar.es/record/153131/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:153131 articulos driver 2025-10-17-14:27:22 ARTICLE