• Medientyp: E-Artikel
  • Titel: Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning
  • Beteiligte: Abdrabou, Ahmed Mohamed Mostafa; Sy, Issa; Bischoff, Markus; Arroyo, Manuel J.; Becker, Sören L.; Mellmann, Alexander; von Müller, Lutz; Gärtner, Barbara; Berger, Fabian K.
  • Erschienen: Springer Science and Business Media LLC, 2023
  • Erschienen in: European Journal of Clinical Microbiology & Infectious Diseases, 42 (2023) 11, Seite 1373-1381
  • Sprache: Englisch
  • DOI: 10.1007/s10096-023-04665-y
  • ISSN: 0934-9723; 1435-4373
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  • Beschreibung: AbstractHypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.