{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rodriguez A"],"funding":["Barri Casanovas Private Foundation Scholarship"],"pagination":["968"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11504409"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(10)"],"pubmed_abstract":["<b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: Of the 8"],"journal":["Antibiotics (Basel, Switzerland)"],"pubmed_title":["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."],"pmcid":["PMC11504409"],"funding_grant_id":["FBC02/2023"],"pubmed_authors":["Sole-Violan J","Guardiola JJ","Gonzalez Garzon C","Sancho S","Estella A","Borges-Sa M","Franquet A","Zaragoza R","Berrueta J","Rodriguez A","Gomez J","Trefler S","Ales F","Bodi M","Cortes L","Gomez-Bertomeu F","Suberviola B","GETGAG/COVID-19 SEMICYUC Working Group","Diaz E","Martin-Loeches I","Claverias L","Garcia-Martinez A"],"additional_accession":[]},"is_claimable":false,"name":"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.","description":"<b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: Of the 8","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2025-04-04T02:21:37.838Z","creation":"2025-04-04T02:21:37.838Z"},"accession":"S-EPMC11504409","cross_references":{"pubmed":["39452234"],"doi":["10.3390/antibiotics13100968"]}}