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Background <p><i>Clostridioides difficile</i> is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS–based workflow coupled with machine learning to distinguish epidemic toxigenic ribotypes (RT027 and RT181) from other strains in real time.</p> Results <p>We analyzed MALDI-TOF spectra from 379 clinical isolates collected across ten Spanish hospitals and identified seven discriminant biomarker peaks. Two peaks (2463 and 4993&#xa0;m/z) were uniquely associated with RT027, while combinations of five additional peaks reliably identified RT181. Our classifiers–implemented both in the commercial Clover MSDAS platform and the open-access AutoCdiff web tool–achieved up to 100% balanced accuracy in ribotype assignment and proved robust in real-time outbreak simulations.</p> Conclusions <p>This study demonstrates that MALDI-TOF MS combined with tailored machine learning can deliver rapid, high-precision ribotype identification for <i>C.&#xa0;difficile</i>. The freely available AutoCdiff models (<a href="https://bacteria.id">https://bacteria.id</a>) offer an immediately deployable solution for clinical laboratories, with the potential to enhance outbreak surveillance and control.</p>

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Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月17日

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  • Mario Blázquez-Sánchez,
  • Alejandro Guerrero-López,
  • Ana Candela,
  • Albert Belenguer-Llorens,
  • José Miguel Moreno,
  • Carlos Sevilla-Salcedo,
  • María Sánchez-Cueto,
  • Manuel J. Arroyo,
  • Mark Gutiérrez-Pareja,
  • Vanessa Gómez-Verdejo,
  • Pablo M. Olmos,
  • Luis Mancera,
  • Patricia Muñoz,
  • Mercedes Marín,
  • Luis Alcalá,
  • David Rodríguez-Temporal,
  • Belén Rodríguez-Sánchez

摘要

Background

Clostridioides difficile is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS–based workflow coupled with machine learning to distinguish epidemic toxigenic ribotypes (RT027 and RT181) from other strains in real time.

Results

We analyzed MALDI-TOF spectra from 379 clinical isolates collected across ten Spanish hospitals and identified seven discriminant biomarker peaks. Two peaks (2463 and 4993 m/z) were uniquely associated with RT027, while combinations of five additional peaks reliably identified RT181. Our classifiers–implemented both in the commercial Clover MSDAS platform and the open-access AutoCdiff web tool–achieved up to 100% balanced accuracy in ribotype assignment and proved robust in real-time outbreak simulations.

Conclusions

This study demonstrates that MALDI-TOF MS combined with tailored machine learning can deliver rapid, high-precision ribotype identification for C. difficile. The freely available AutoCdiff models (https://bacteria.id) offer an immediately deployable solution for clinical laboratories, with the potential to enhance outbreak surveillance and control.