School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
Department of Internal Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, Taiwan.
Int J Antimicrob Agents. 2024 Nov;64(5):107329. doi: 10.1016/j.ijantimicag.2024.107329. Epub 2024 Sep 6.
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.
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.
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.
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.
使用基质辅助激光解吸/电离-飞行时间质谱（MALDI-TOF MS）结合机器学习（ML）已被探索用于预测抗生素耐药性。本研究评估了 MALDI-TOF MS 与各种 ML 分类器结合的有效性，并建立了预测金黄色葡萄球菌抗生素耐药性和 mecA 基因存在的最佳模型。
使用数据库中的数据分析金黄色葡萄球菌对抗生素的耐药性和 MALDI-TOF MS，数据库中的数据来自抗生素对抗微生物的 MALDI-TOF 质谱分析数据库（DRIAMS）和一个医疗中心（CS 数据库）。使用五种 ML 分类器分析性能指标。Shapley 值量化了各个特征的预测贡献。
LightGBM 在预测耐甲氧西林金黄色葡萄球菌（ORSA）中大多数 1 级抗生素的耐药性方面表现优于所有金黄色葡萄球菌和耐甲氧西林金黄色葡萄球菌（OSSA），在两个数据库中均表现优于所有金黄色葡萄球菌和耐甲氧西林金黄色葡萄球菌（OSSA）。在 DRIAMS 中，多层感知器（MLP）与极好的预测性能相关，表现为准确性/AUROC/AUPR，对于克林霉素（0.74/0.81/0.90）、四环素（0.86/0.87/0.94）和甲氧苄啶-磺胺甲恶唑（0.95/0.72/0.97）。在 CS 数据库中，Ada 和 Light Gradient Boosting Machine（LightGBM）对红霉素（0.97/0.92/0.86）和四环素（0.68/0.79/0.86）表现出极好的性能。质荷比（m/z）特征 2411-2414 和 2429-2432 与克林霉素耐药性相关，而 5033-5036 与 DRIAMS 中的红霉素耐药性相关。在 CS 数据库中，重叠特征 2423-2426、4496-4499 和 3764-3767 同时预测 mecA 和耐苯唑西林的存在。
使用 MALDI-TOF MS 预测金黄色葡萄球菌的抗生素耐药性的预测性能取决于数据库的特征和选择的 ML 算法。特定和重叠的质谱特征是 mecA 和特定抗生素耐药性的极好预测标志物。
