Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase)

Carbapenemase-producing Enterobacterales (CPE) present limited therapeutic options. Optimal treatment requires identifying the carbapenemase type, often requiring confirmatory testing beyond routine susceptibility results. We develop MALCA, a machine-learning classifier that uses routine disc diffusion antibiogram results to directly detect CPE and identify the carbapenemase type. From 11,992 clinical isolates, we build a stepwise random-forest pipeline and derive two classifiers based on panels of 22 or 8 antibiotics (MALCA-22 and MALCA-8). In an external validation study involving 8514 isolates, both MALCA classifiers achieved sensitivity and specificity >96% for CPE detection, outperforming European and French algorithms developed for CPE screening. For the most prevalent carbapenemases, MALCA achieve sensitivities exceeding 97% and specificities above 98%, particularly for OXA-48-like, NDM, and KPC producers. MALCA is a rapid, and inexpensive diagnostic tool that uses solid antibiogram data to detect and type CPE, enabling earlier targeted therapy and diagnostic guidance without additional reagents or human resources.

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Publication Details

Journal
Nature Communications
Published
2026-05-11
DOI
https://doi.org/10.1038/s41467-026-72713-0
Primary Topic
Bacterial Identification and Susceptibility Testing
Type
article
Field-Weighted Citation Impact
0.00

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article

Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase)

Inès Rezzoug, Yahia Benzerara, Agnès B. Jousset, Rémy A. Bonnin et al.
Nature Communications
Bacterial Identification and Susceptibility Testing
article

Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase)

Inès Rezzoug, Yahia Benzerara, Agnès B. Jousset, Rémy A. Bonnin, Corentin Poignon, Alexandre Godmer, Alexandra Aubry, Cécile Emeraud, Hippolyte De Swardt, Lena Latour, Alice Pages, Sarah Ronsin, L Dortet, Mariette Matondo, Quentin Giai Gianetto, Nicolas Veziris
article en

Abstract

Carbapenemase-producing Enterobacterales (CPE) present limited therapeutic options. Optimal treatment requires identifying the carbapenemase type, often requiring confirmatory testing beyond routine susceptibility results. We develop MALCA, a machine-learning classifier that uses routine disc diffusion antibiogram results to directly detect CPE and identify the carbapenemase type. From 11,992 clinical isolates, we build a stepwise random-forest pipeline and derive two classifiers based on panels of 22 or 8 antibiotics (MALCA-22 and MALCA-8). In an external validation study involving 8514 isolates, both MALCA classifiers achieved sensitivity and specificity >96% for CPE detection, outperforming European and French algorithms developed for CPE screening. For the most prevalent carbapenemases, MALCA achieve sensitivities exceeding 97% and specificities above 98%, particularly for OXA-48-like, NDM, and KPC producers. MALCA is a rapid, and inexpensive diagnostic tool that uses solid antibiogram data to detect and type CPE, enabling earlier targeted therapy and diagnostic guidance without additional reagents or human resources.

Nature Communications
Centre National de la Recherche Scientifique (FR), Inserm (FR), Institut Pasteur (FR), Université Paris Cité (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Hôpital Saint-Antoine (FR), Laboratoire National de Référence (MA), CEA Paris-Saclay (FR), Centre d'Immunologie et des Maladies Infectieuses (FR), CNR de la Résistance aux Antibiotiques (FR), Centre de Recherche Saint-Antoine (FR), Bicêtre Hospital (FR), Egis (France) (FR)
Institut Français de Bioinformatique, Agence Nationale de la Recherche
Openalex Percentile: Top 7%
Bacterial Identification and Susceptibility Testing
DOIOpen Access PDFOpenAlex
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