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.
Similar content being viewed by others
Acknowledgements
The bioinformatics analyses were performed on the Core Cluster of the Institut Français de Bioinformatique (IFB). The SEPSIS Comprehensive Center—IHU SEPSIS was supported by the French National Research Agency-France 2030 programme (grant number ANR-23-IAHU-0004, awarded to L.D. and C.E.).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
A.G., C.E., Y.B., and L.D. are co-inventors on a patent application covering the MALCA algorithm and associated datasets. The remaining authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Emeraud, C., Benzerara, Y., De Swardt, H. et al. Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase). Nat Commun (2026). https://doi.org/10.1038/s41467-026-72713-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-72713-0
