TY - JOUR
T1 - Rapid detection of heterogeneous vancomycin-intermediate staphylococcus aureusbased on matrix-assisted laser desorption ionization time-of-flight
T2 - Using a machine learning approach and unbiased validation
AU - Wang, Hsin Yao
AU - Chen, Chun Hsien
AU - Lee, Tzong Yi
AU - Horng, Jorng Tzong
AU - Liu, Tsui Ping
AU - Tseng, Yi Ju
AU - Lu, Jang Jih
N1 - Publisher Copyright:
© 2007 - 2018 Frontiers Media S.A. All Rights Reserved.
PY - 2018/10/11
Y1 - 2018/10/11
N2 - Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile-area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.
AB - Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile-area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.
KW - Heterogeneous vancomycin-intermediate staphylococcus aureus
KW - Machine learning
KW - Matrix-assisted laser desorption ionization (MALDI) mass spectrometry
KW - Rapid detection
KW - Vancomycin intermediate S. Aureus (VISA)
UR - http://www.scopus.com/inward/record.url?scp=85055353009&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2018.02393
DO - 10.3389/fmicb.2018.02393
M3 - 期刊論文
AN - SCOPUS:85055353009
SN - 1664-302X
VL - 9
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
IS - OCT
M1 - 2393
ER -