Articles producció científicaBioquímica i Biotecnologia

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

  • Dades identificatives

    Identificador:  imarina:9390033
    Autors:  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
    Resum:
    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.
  • Altres:

    Enllaç font original: https://www.mdpi.com/2079-6382/13/10/968
    Referència de l'ítem segons les normes 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
    Referència a l'article segons font original: Antibiotics. 13 (10): 968-
    DOI de l'article: 10.3390/antibiotics13100968
    Any de publicació de la revista: 2024
    Entitat: Universitat Rovira i Virgili
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Data d'alta del registre: 2025-02-19
    Autor/s de la URV: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep / Gomez Bertomeu, Frederic-Francesc / Rodríguez Oviedo, Alejandro Hugo
    Departament: Bioquímica i Biotecnologia
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Tipus de publicació: Journal Publications
    Autor segons l'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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Àrees temàtiques: 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
    Adreça de correu electrònic de l'autor: josep.gomez@urv.cat, frederic-francesc.gomez@urv.cat, alejandrohugo.rodriguez@urv.cat, mariaamparo.bodi@urv.cat, mariaamparo.bodi@urv.cat
  • Paraules clau:

    Machine learning
    Machine learnin
    Influenza a (h1n1)
    Fungal coinfection
    Covid-19
    Bacterial coinfection
    Biochemistry
    Infectious Diseases
    Microbiology
    Microbiology (Medical)
    Pharmacology & Pharmacy
    Pharmacology (Medical)
    Pharmacology
    Toxicology and Pharmaceutics (Miscellaneous)
    toxicology and pharmaceutics (all)
    General pharmacology
    toxicology and pharmaceutics
    Engenharias ii
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