Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections.

Journal: NPJ digital medicine
Published Date: May 29, 2025

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.

Authors

  • Cecilia Bonazzetti
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Ettore Rocchi
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Alice Toschi
    Infectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
  • Nicolas Riccardo Derus
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Claudia Sala
    Monoclonal Antibody Discovery Laboratory, Fondazione Toscana Life Sciences, Siena, Italy. c.sala@toscanalifesciences.org.
  • Renato Pascale
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Matteo Rinaldi
    Department of Medical and Surgical Sciences, University of Bologna, S. Orsola-Malpighi Hospital, Bologna 40138, Italy.
  • Caterina Campoli
    Infectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
  • Zeno Adrien Igor Pasquini
    Infectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
  • Beatrice Tazza
    Infectious Diseases Unit, Department of Integrated Infectious Risk Management, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
  • Armando Amicucci
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Milo Gatti
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Simone Ambretti
    Section of Microbiology, Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
  • Pierluigi Viale
    Department of Medical and Surgical Sciences, University of Bologna, S. Orsola-Malpighi Hospital, Bologna 40138, Italy.
  • Gastone Castellani
    Department of Specialised, Diagnostic and Experimental Medicine, University of Bologna, 40126, Bologna, BO, Italy.
  • Maddalena Giannella
    Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy. maddalena.giannella@unibo.it.

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