Repurposing MALDI-TOF MS for effective antibiotic resistance screening in Staphylococcus epidermidis using machine learning.

The emergence of Staphylococcus epidermidis as a significant nosocomial pathogen necessitates advancements in more efficient antimicrobial resistance profiling. However, existing culture-based and PCR-based antimicrobial susceptibility testing methods are far too slow or costly. This study combines machine learning with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to develop predictive models for various antibiotics using a comprehensive dataset containing thousands of S. epidermidis isolates. Optimized machine learning models utilized feature selection and achieved high AUROC scores ranging from 0.80 to 0.95 while maintaining AUPRC scores up to 0.97. Shapley Additive exPlanations were employed to analyze relevant features and assess the significance of corresponding protein biomarkers while also verifying that predictive power was derived from the detection of proteins rather than noise. Antimicrobial resistance models were validated externally to evaluate model performance outside the original data collection site. The approaches and findings in this study demonstrate a significant advancement in rapid, cost-effective antimicrobial resistance profiling, offering a promising solution for improving treatments for nosocomial infections and being potentially applicable to other microbial pathogens in the future.