Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections
Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections
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Author / Creator
Bonazzetti, Cecilia , Rocchi, Ettore , Toschi, Alice , Derus, Nicolas Riccardo , Sala, Claudia , Pascale, Renato , Rinaldi, Matteo , Campoli, Caterina , Pasquini, Zeno Adrien Igor , Tazza, Beatrice , Amicucci, Armando , Gatti, Milo , Ambretti, Simone , Viale, Pierluigi , Castellani, Gastone and Giannella, Maddalena
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Contents
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....
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Full title
Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections
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TN_cdi_doaj_primary_oai_doaj_org_article_a40159d052524c8aaf547e48156635be
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a40159d052524c8aaf547e48156635be
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ISSN
2398-6352
E-ISSN
2398-6352
DOI
10.1038/s41746-025-01696-x
