Author, as appears in the article.: Rodriguez, Alejandro; Gomez, Josep; Martin-Loeches, Ignacio; Claverias, Laura; Diaz, Emili; Zaragoza, Rafael; Borges-Sa, Marcio; Gomez-Bertomeu, Frederic; Franquet, Alvaro; Trefler, Sandra; Garzon, Carlos Gonzalez; Cortes, Lissett; Ales, Florencia; Sancho, Susana; Sole-Violan, Jordi; Estella, Angel; Berrueta, Julen; Garcia-Martinez, Alejandro; Suberviola, Borja; Guardiola, Juan J; Bodi, Maria
Department: Bioquímica i Biotecnologia
URV's Author/s: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep / Gomez Bertomeu, Frederic-Francesc / Rodríguez Oviedo, Alejandro Hugo
Keywords: Machine learning Machine learnin Influenza a (h1n1) Fungal coinfection Covid-19 Bacterial coinfection
Abstract: Background: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. Methods: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. Results: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. Conclusions: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.
Thematic Areas: Pharmacology, toxicology and pharmaceutics (miscellaneous) Pharmacology, toxicology and pharmaceutics (all) Pharmacology (medical) Pharmacology & pharmacy Microbiology (medical) Microbiology Infectious diseases General pharmacology, toxicology and pharmaceutics Engenharias ii Biochemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: josep.gomez@urv.cat frederic-francesc.gomez@urv.cat alejandrohugo.rodriguez@urv.cat mariaamparo.bodi@urv.cat mariaamparo.bodi@urv.cat
Author identifier: 0000-0002-0573-7621 0000-0002-8039-2889 0000-0001-8828-5984 0000-0001-7652-8379 0000-0001-7652-8379
Record's date: 2025-02-19
Paper version: info:eu-repo/semantics/publishedVersion
Paper original source: Antibiotics. 13 (10): 968-
APA: Rodriguez, Alejandro; Gomez, Josep; Martin-Loeches, Ignacio; Claverias, Laura; Diaz, Emili; Zaragoza, Rafael; Borges-Sa, Marcio; Gomez-Bertomeu, Frede (2024). A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective. Antibiotics, 13(10), 968-. DOI: 10.3390/antibiotics13100968
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Publication Type: Journal Publications
Repositori URV