Accurate prediction of antimicrobial resistance and genetic marker of Staphylococcus aureus clinical isolates using MALDI-TOF MS and machine learning – across DRIAMS and Taiwan database

Background

The use of matrix-assisted laser desorption/ionisation-time-of-flight mass spectra (MALDI-TOF MS) with machine learning (ML) has been explored for predicting antimicrobial resistance. This study evaluates the effectiveness of MALDI-TOF MS paired with various ML classifiers and establishes optimal models for predicting antimicrobial resistance and the presence of mecA gene among Staphylococcus aureus.

Materials and Methods

Antimicrobial resistance against tier 1 antibiotics and MALDI-TOF MS of S. aureus were analysed using data from the Database of Resistance against Antimicrobials with MALDI-TOF Mass Spectrometry (DRIAMS) and one medical centre (CS database). Five ML classifiers were used to analyse performance metrics. The Shapley value quantified the predictive contribution of individual features.

Results

The LightGBM demonstrated superior performance in predicting antimicrobial resistance for most tier 1 antibiotics among oxacillin-resistant S. aureus (ORSA) compared with all S. aureus and oxacillin-susceptible S. aureus (OSSA) in both databases. In DRIAMS, Multilayer Perceptron (MLP) was associated with excellent predictive performance, expressed as accuracy/AUROC/AUPR, for clindamycin (0.74/0.81/0.90), tetracycline (0.86/0.87/0.94), and trimethoprim-sulfamethoxazole (0.95/0.72/0.97). In the CS database, Ada and Light Gradient Boosting Machine (LightGBM) showed excellent performance for erythromycin (0.97/0.92/0.86) and tetracycline (0.68/0.79/0.86). Mass-to-charge ratio (m/z) features of 2411–2414 and 2429–2432 correlated with clindamycin resistance, whereas 5033–5036 was linked to erythromycin resistance in DRIAMS. In the CS database, overlapping features of 2423–2426, 4496–4499, and 3764–3767 simultaneously predicted the presence of mecA and oxacillin resistance.

Conclusion

The predictive performance of antimicrobial resistance against S. aureus using MALDI-TOF MS depends on database characteristics and the ML algorithm selected. Specific and overlapping mass spectra features are excellent predictive markers for mecA and specific antimicrobial resistance.