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<p>Carbapenemase-producing <i>Enterobacterales</i> (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 &gt;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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

摘要

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.