CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests

Linea Katharina Muhsal, Cansu Cimen, Janko Sattler, Lisa Theis, Oliver Nolte, Laurent Dortet, Rémy A. Bonnin, Adrian Egli and Axel Hamprecht ()
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Linea Katharina Muhsal: Carl von Ossietzky University Oldenburg
Cansu Cimen: Carl von Ossietzky University Oldenburg
Janko Sattler: University of Cologne
Lisa Theis: Carl von Ossietzky University Oldenburg
Oliver Nolte: University of Zurich
Laurent Dortet: Université Paris-Saclay, CEA, LabEx LERMIT
Rémy A. Bonnin: Université Paris-Saclay, CEA, LabEx LERMIT
Adrian Egli: University of Zurich
Axel Hamprecht: Carl von Ossietzky University Oldenburg

Nature Communications, 2025, vol. 16, issue 1, 1-7

Abstract: Abstract Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carbapenemase production from inhibition zone diameters of eight antibiotics, using 385 isolates for training with whole genome sequencing as reference. Validation on two external datasets (A = 282, B = 518 isolates) shows high performance: sensitivity/specificity are 96.6%/84.4% (training), 96.3%/86.1% (A), and 91.2%/87.0% (B, five antibiotics). In contrast, the algorithms of EUCAST and the Antibiogram Committee of the French Society of Microbiology (CA-SFM) exhibit lower specificity (8.2% and 40.1%, respectively on the training dataset). In this work, we show that CarbaDetector, available as a web-app, reduces unnecessary confirmatory testing and accelerates the time to result. This approach offers high sensitivity and improved specificity compared to standard algorithms and has the potential to improve CPE detection, especially in resource-limited settings.

Date: 2025
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DOI: 10.1038/s41467-025-66183-z

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