|Title||Rapid identification and discrimination of methicillin-resistant Staphylococcus aureus strains via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Liu X, Su T, Hsu Y-MS, Yu H, Yang HSarina, Jiang L, Zhao Z|
|Journal||Rapid Commun Mass Spectrom|
|Date Published||2021 Jan 30|
|Keywords||Algorithms, Humans, Machine Learning, Methicillin-Resistant Staphylococcus aureus, Molecular Typing, Sensitivity and Specificity, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Staphylococcal Infections, Staphylococcus aureus|
RATIONALE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
|Alternate Journal||Rapid Commun Mass Spectrom|
He Sarina Yang, Ph.D. Zhen Zhao, Ph.D.