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<p>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 <i>E. coli, Klebsiella</i> spp, and <i>Proteus</i> 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 (<a href="https://github.com/EttoreRocchi/ResPredAI">https://github.com/EttoreRocchi/ResPredAI</a>), along with documentation for running the same workflow on a different dataset, to account for local epidemiology and clinical features.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月29日

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  • 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

摘要

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.