MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

Abstract

As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus. Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/.

Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia

“…Chung et al developed a web tool for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant S. aureus [ 66 ]. They also used MALDI-TOF MS with ML, i.e., DT, RF and SVM, models to classify whether these S. aureus bacteria were resistant.…”

“…Thus, the number of features found Chung et al’s study may not fit optimal parameter combination. In addition, Chung et al used the grid search to find the optimal parameters for ML models [ 66 ]. When the model has many parameters, the grid search may require a lot of computation time.…”

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

“…The primary objective of this study was to compare different preprocessing methods for MS. Almost all parameters used the default provided by the scikit-learn package [ 30 ], except some parameters that were used according to our previous research [ 4 , 22 ]. Specifically, the L2-norm was specified in the penalty and the maximum number of iterations was set to 1000 when LR models were constructed.…”

“…The set of peaks after signal preprocessing had a significant influence on the entire study. In recent years, several studies have been conducted on the identification of antibiotic resistance using MALDI-TOF MS [ 4 , 9 , 10 , 19 , 20 , 21 , 22 , 23 ]. These studies chose a proper preprocessing method to detect peaks and analyze the information from the spectra.…”

“…Several studies have confirmed that MALDI-TOF MS is a good method for solving bacteria-related problems [ 4 , 9 , 10 , 19 , 20 , 21 , 22 , 23 ]. In antibiotic resistance-related studies, the combination of MS and machine learning methods appears to be popular and useful for the detection of antibiotic resistance.…”