ArticleNature communications2026
Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase).
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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