Rapid, automatic typing of Clostridioides difficile Ribotypes Using MALDI-TOF MS

Clostridioides difficile is a major cause of hospital-acquired diarrhea, posing significant clinical challenges due to its high mortality rates and its involvement in nosocomial outbreaks. Detecting its toxigenic ribotypes (RTs) rapidly and accurately is crucial for effective management and preventing fatal outcomes. This research aimed to create a methodology based on MALDI-TOF MS and Machine Learning (ML) algorithms to differentiate C. difficile RTs. MALDI-TOF spectra were acquired from 363 clinical isolates sourcing from 10 Spanish hospitals and analysed using Clover MSDAS and AutoCdiff, an ad hoc software developed in this study. Experiments confirmed seven biomarker peaks differentiating RT027 and RT181 from other RTs. Automatic classification tools in Clover MSDAS and AutoCdiff showed up to 100% balanced accuracy, even for isolates from real-time outbreaks. The developed models, available on the AutoCdiff website --, offer researchers a valuable tool for quick RT determination. This approach significantly reduces time, costs, and hands-on time. ### Competing Interest Statement The authors have declared no competing interest.