biostudies-literatureRodriguez ABarri Casanovas Private Foundation Scholarship968https://www.ebi.ac.uk/biostudies/studies/S-EPMC11504409biostudies-literatureUnknown13(10)<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 8Antibiotics (Basel, Switzerland)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.PMC11504409FBC02/2023Sole-Violan JGuardiola JJGonzalez Garzon CSancho SEstella ABorges-Sa MFranquet AZaragoza RBerrueta JRodriguez AGomez JTrefler SAles FBodi MCortes LGomez-Bertomeu FSuberviola BGETGAG/COVID-19 SEMICYUC Working GroupDiaz EMartin-Loeches IClaverias LGarcia-Martinez AfalseA 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.<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 82024-01-01T00:00:00Z2024 Oct2025-04-04T02:21:37.838Z2025-04-04T02:21:37.838ZS-EPMC115044093945223410.3390/antibiotics13100968