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.
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引用次数: 0
Abstract
Background: The use of matrix-assisted laser desorption/ionization-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 mecA gene existence among Staphylococcus aureus.
Materials and methods: The antimicrobial resistance against tier 1 antibiotics and MALDI-TOF MS of S. aureus were analyzed using data from the Database of Resistance against Antimicrobials with MALDI-TOF Mass Spectrometry (DRIAMS) and one medical center (CS database). Five ML classifiers were used to analyze performance metrics. The Shapley value quantified the predictive contribution of individual feature.
Results: The LightGBM demonstrated superior performance in predicting antimicrobial resistance for most tier 1 antibiotics among oxacillin-resistant S. aureus (ORSA) than all and oxacillin-susceptible S. aureus (OSSA) in both databases. In DRIAMS, MLP encompassed 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 CS database, Ada and LightGBM retained excellent performance for erythromycin (0.97/0.92/0.86) and tetracycline (0.68/0.79/0.86), respectively. Mass-to-charge ratio (m/z) features of 2,411-2,414 and 2,429-2,432 correlated with clindamycin resistance, while 5,033-5,036 was linked to erythromycin resistance in DRIAMS. In CS database, overlapping features of 2,423-2,426, 4,496-4,499, and 3,764-3,767 simultaneously predicted mecA existence and oxacillin resistance.
Conclusion: The predictive performance of antimicrobial resistance against S. aureus using MALDI-TOF MS depends on database characteristics and ML algorithm selected. Specific and overlapping MS features are excellent predictive markers for mecA and specific antimicrobial resistance.
期刊介绍:
The International Journal of Antimicrobial Agents is a peer-reviewed publication offering comprehensive and current reference information on the physical, pharmacological, in vitro, and clinical properties of individual antimicrobial agents, covering antiviral, antiparasitic, antibacterial, and antifungal agents. The journal not only communicates new trends and developments through authoritative review articles but also addresses the critical issue of antimicrobial resistance, both in hospital and community settings. Published content includes solicited reviews by leading experts and high-quality original research papers in the specified fields.