Rapid identification of MRSA using mass spectrometry and machine learning from over 20000 clinical isolates

Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a MALDI-TOF MS based machine learning model for rapidly MRSA prediction, the model was evaluated on a prospective test and four external clinical sites. On the dataset comprising 20359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78–0.88. Our MALDI–TOF MS-based ML model for the rapid MRSA identification can be easily integrated into the current clinical workflows and can further support physicians prescribe proper antibiotic treatments.