Integrating MALDI-TOF Mass Spectrometry and Machine Learning for Rapid and Clinically Relevant Differentiation of MRSA and MSSA

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is routinely used in clinical microbiology for rapid species identification; however, its potential to support early antimicrobial decision-making remains under active investigation. Rapid discrimination of methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) and methicillin-susceptible <i>Staphylococcus aureus</i> (MSSA) at the time of identification could facilitate earlier optimization of antimicrobial therapy and infection control measures. In this study, MALDI-TOF MS spectral data were analyzed to evaluate supervised machine learning-based differentiation of MRSA and MSSA. A total of 91 <i>S. aureus</i> isolates (37 MRSA and 54 MSSA) were included, with methicillin susceptibility determined by the cefoxitin disk diffusion test according to EUCAST guidelines and used as the reference standard. MALDI-TOF MS spectra were acquired following standard on-plate extraction, subjected to quality control, and preprocessed prior to analysis. Principal component analysis demonstrated partial but consistent separation between MRSA and MSSA isolates. A Random Forest classifier was trained and validated using stratified 10-fold cross-validation, achieving an overall classification accuracy of 81.3% and a receiver operating characteristic area under the curve of 0.916. Class-specific analysis revealed high precision for MRSA (95.5%) and excellent recall for MSSA (98.1%). These findings indicate that MALDI-TOF MS combined with machine learning can provide clinically relevant information for rapid MRSA/MSSA differentiation and may serve as a cost-free decision-support approach in routine clinical microbiology workflows, complementing standard phenotypic susceptibility testing.