Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates.

ABSTRACT: Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS)-based machine learning model for rapid MRSA prediction. The model was evaluated on a prospective test and four external clinical sites. For the data set comprising 20,359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78 to 0.88. These results were further interpreted using shapely additive explanations and presented using the pseudogel method. The important MRSA feature, m/z 6,590 to 6,599, was identified as a UPF0337 protein SACOL1680 with a lower binding affinity or no docking

SUBMITTER: Yu J 

PROVIDER: S-EPMC9045122 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature