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

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    Identifier:  imarina:9390033
    Authors:  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
    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.
  • Others:

    Link to the original source: https://www.mdpi.com/2079-6382/13/10/968
    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
    Paper original source: Antibiotics. 13 (10): 968-
    Article's DOI: 10.3390/antibiotics13100968
    Journal publication year: 2024
    Entity: Universitat Rovira i Virgili
    Paper version: info:eu-repo/semantics/publishedVersion
    Record's date: 2025-02-19
    URV's Author/s: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep / Gomez Bertomeu, Frederic-Francesc / Rodríguez Oviedo, Alejandro Hugo
    Department: Bioquímica i Biotecnologia
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Publication Type: Journal Publications
    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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    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
    Author's mail: josep.gomez@urv.cat, frederic-francesc.gomez@urv.cat, alejandrohugo.rodriguez@urv.cat, mariaamparo.bodi@urv.cat, mariaamparo.bodi@urv.cat
  • Keywords:

    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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