Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections
Abstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third g...
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01696-x |
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| author | Cecilia Bonazzetti Ettore Rocchi Alice Toschi Nicolas Riccardo Derus Claudia Sala Renato Pascale Matteo Rinaldi Caterina Campoli Zeno Adrien Igor Pasquini Beatrice Tazza Armando Amicucci Milo Gatti Simone Ambretti Pierluigi Viale Gastone Castellani Maddalena Giannella |
| author_facet | Cecilia Bonazzetti Ettore Rocchi Alice Toschi Nicolas Riccardo Derus Claudia Sala Renato Pascale Matteo Rinaldi Caterina Campoli Zeno Adrien Igor Pasquini Beatrice Tazza Armando Amicucci Milo Gatti Simone Ambretti Pierluigi Viale Gastone Castellani Maddalena Giannella |
| author_sort | Cecilia Bonazzetti |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third generation cephalosporins (3GC-R), beta-lactam/beta-lactamase inhibitors (BL/BLI-R) and carbapenems (C-R) was performed. Analyses were carried out within a machine learning framework, developed using the scikit-learn Python package. Overall, 2552 patients were included. Enterobacterales accounted for 85.5% of isolates, with E. coli, Klebsiella spp, and Proteus spp being most common. Distribution of resistance was FQ-R 48.6%, 3GC-R 40.1%, BL/BLI-R 29.9%, and C-R 16.9%. Models’ validation showed good performance predicting antibiotic resistance for all four resistance classes, with the best performance for C-R (AUC-ROC 0.921 ± 0.013). The developed pipeline has been made available ( https://github.com/EttoreRocchi/ResPredAI ), along with documentation for running the same workflow on a different dataset, to account for local epidemiology and clinical features. |
| format | Article |
| id | doaj-art-a40159d052524c8aaf547e48156635be |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-a40159d052524c8aaf547e48156635be2025-08-20T03:16:47ZengNature Portfolionpj Digital Medicine2398-63522025-05-01811610.1038/s41746-025-01696-xArtificial Intelligence model to predict resistances in Gram-negative bloodstream infectionsCecilia Bonazzetti0Ettore Rocchi1Alice Toschi2Nicolas Riccardo Derus3Claudia Sala4Renato Pascale5Matteo Rinaldi6Caterina Campoli7Zeno Adrien Igor Pasquini8Beatrice Tazza9Armando Amicucci10Milo Gatti11Simone Ambretti12Pierluigi Viale13Gastone Castellani14Maddalena Giannella15Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaInfectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaInfectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di BolognaInfectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di BolognaInfectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaSection of Microbiology, Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of BolognaAbstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third generation cephalosporins (3GC-R), beta-lactam/beta-lactamase inhibitors (BL/BLI-R) and carbapenems (C-R) was performed. Analyses were carried out within a machine learning framework, developed using the scikit-learn Python package. Overall, 2552 patients were included. Enterobacterales accounted for 85.5% of isolates, with E. coli, Klebsiella spp, and Proteus spp being most common. Distribution of resistance was FQ-R 48.6%, 3GC-R 40.1%, BL/BLI-R 29.9%, and C-R 16.9%. Models’ validation showed good performance predicting antibiotic resistance for all four resistance classes, with the best performance for C-R (AUC-ROC 0.921 ± 0.013). The developed pipeline has been made available ( https://github.com/EttoreRocchi/ResPredAI ), along with documentation for running the same workflow on a different dataset, to account for local epidemiology and clinical features.https://doi.org/10.1038/s41746-025-01696-x |
| spellingShingle | Cecilia Bonazzetti Ettore Rocchi Alice Toschi Nicolas Riccardo Derus Claudia Sala Renato Pascale Matteo Rinaldi Caterina Campoli Zeno Adrien Igor Pasquini Beatrice Tazza Armando Amicucci Milo Gatti Simone Ambretti Pierluigi Viale Gastone Castellani Maddalena Giannella Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections npj Digital Medicine |
| title | Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections |
| title_full | Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections |
| title_fullStr | Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections |
| title_full_unstemmed | Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections |
| title_short | Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections |
| title_sort | artificial intelligence model to predict resistances in gram negative bloodstream infections |
| url | https://doi.org/10.1038/s41746-025-01696-x |
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