Analysis of high-molecular-weight proteins using MALDI-TOF MS and machine learning for the differentiation of clinically relevant Clostridioides difficile ribotypes.

PURPOSE

Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach. This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS.

METHODS

Sixty-nine isolates representative of the most common ribotypes in Europe were analyzed in the 17,000-65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and 'Other RTs'). Five supervised Machine Learning algorithms were tested for this purpose: K-Nearest Neighbors, Support Vector Machine, Partial Least Squares-Discriminant Analysis, Random Forest (RF) and Light-Gradient Boosting Machine (GBM).

RESULTS

All algorithms yielded cross-validation results > 70%, being RF and Light-GBM the best performing, with 88% of agreement. Area under the ROC curve of these two algorithms was > 0.9. RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not be differentiated from RT027.

CONCLUSIONS

This study shows the possibility of rapid discrimination of relevant C. difficile ribotypes by using MALDI-TOF MS. This methodology reduces the time, costs and laboriousness of current reference methods.