Department of Laboratory Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China.
Proteomics & Metabolomics Core Facility, Weill Cornell Medicine, New York, NY 10065, USA.
Rapid Commun Mass Spectrom. 2021 Jan 30;35(2):e8972. doi: 10.1002/rcm.8972.
Methicillin-resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital- and community-acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in-hospital mortality. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI-TOF MS-based methodology for MRSA screening with machine-learning algorithms.
A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin-sensitive S. aureus (MSSA). The mass-to-charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non-linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model.
A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non-linear RBF-SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF-SVM) and 0.82 (RF).
Our study demonstrates that MALDI-TOF MS coupled with machine-learning algorithms could be used to develop a rapid and easy-to-use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost-effective and time-efficient alternative to conventional resistance detection in the future to improve clinical treatment.
耐甲氧西林金黄色葡萄球菌（MRSA）是全球范围内导致医院获得性和社区获得性感染的主要临床病原体之一。抗生素靶向治疗的延迟会导致住院时间延长、成本增加和住院死亡率上升。基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）在过去十年中已被纳入微生物鉴定的常规工作流程，并且在检测细菌耐药性方面也显示出了有前途的功能。因此，我们描述了一种基于 MALDI-TOF MS 的快速 MRSA 筛选方法，并结合机器学习算法。
本研究共纳入 452 株临床分离的金黄色葡萄球菌，其中 194 株为 MRSA，258 株为甲氧西林敏感金黄色葡萄球菌（MSSA）。通过 Lasso 回归对 MRSA 和 MSSA 菌株的质荷比（m/z）特征进行分箱和选择。然后，使用具有径向基函数（RBF）核的非线性支持向量机（SVM）对这些特征进行训练，以评估判别性能。评估并比较了分类器的准确性、敏感性、特异性和接收者操作特征（ROC）曲线下的面积（AUC）与随机森林（RF）模型的结果。
共鉴定出所有分离株的 2601 个独特光谱峰，选择了 38 个 m/z 特征用于分类模型。非线性 RBF-SVM 模型和 RF 模型的 AUC 分别为 0.89 和 0.87，准确性范围分别为 0.86（RBF-SVM）和 0.82（RF）。
本研究表明，MALDI-TOF MS 结合机器学习算法可用于开发一种快速、易于使用的方法，以区分 MRSA 和 MSSA。鉴于该方法易于在常规微生物学实验室中实施，它为未来提高临床治疗效果提供了一种具有成本效益和高效的替代传统耐药检测的方法。
