1
|
Wang Z, Pang Y, Chung CR, Wang HY, Cui H, Chiang YC, Horng JT, Lu JJ, Lee TY. A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology. Brief Bioinform 2023; 24:bbad330. [PMID: 37742050 DOI: 10.1093/bib/bbad330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/19/2023] [Accepted: 08/31/2023] [Indexed: 09/25/2023] Open
Abstract
The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.
Collapse
Affiliation(s)
- Zhuo Wang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Haiyan Cui
- Department of Clinical Laboratory, Longgang District People's Hospital of Shenzhen & The Second Affiliated Hospital of the Chinese University of Hong Kong, Shenzhen, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 33303, Taiwan
- Department of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| |
Collapse
|
2
|
Chung CR, Wang HY, Chou PH, Wu LC, Lu JJ, Horng JT, Lee TY. Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra. Int J Mol Sci 2023; 24:ijms24020998. [PMID: 36674514 PMCID: PMC9865071 DOI: 10.3390/ijms24020998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.
Collapse
Affiliation(s)
- Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan 333323, Taiwan
| | - Po-Han Chou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 333323, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 333323, Taiwan
| | - Jorng-Tzong Horng
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Correspondence: (J.-T.H.); (T.-Y.L.)
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Correspondence: (J.-T.H.); (T.-Y.L.)
| |
Collapse
|
3
|
Kong PH, Chiang CH, Lin TC, Kuo SC, Li CF, Hsiung CA, Shiue YL, Chiou HY, Wu LC, Tsou HH. Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia. Pathogens 2022; 11:pathogens11050586. [PMID: 35631107 PMCID: PMC9143686 DOI: 10.3390/pathogens11050586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/29/2022] Open
Abstract
Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains’ data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.
Collapse
Affiliation(s)
- Po-Hsin Kong
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (P.-H.K.); (Y.-L.S.)
- Center for Precision Medicine, Chi Mei Medical Center, Tainan 71004, Taiwan;
| | - Cheng-Hsiung Chiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan; (C.-H.C.); (C.A.H.); (H.-Y.C.)
| | - Ting-Chia Lin
- Center for Precision Medicine, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Shu-Chen Kuo
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan;
| | - Chien-Feng Li
- Department of Medical Research, Chi Mei Medical Center, Tainan 71004, Taiwan;
| | - Chao A. Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan; (C.-H.C.); (C.A.H.); (H.-Y.C.)
| | - Yow-Ling Shiue
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (P.-H.K.); (Y.-L.S.)
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan; (C.-H.C.); (C.A.H.); (H.-Y.C.)
- School of Public Health, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan
- Master’s Program in Applied Epidemiology, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan
| | - Li-Ching Wu
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; (P.-H.K.); (Y.-L.S.)
- Center for Precision Medicine, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Correspondence: (L.-C.W.); (H.-H.T.)
| | - Hsiao-Hui Tsou
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan; (C.-H.C.); (C.A.H.); (H.-Y.C.)
- Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung 40402, Taiwan
- Correspondence: (L.-C.W.); (H.-H.T.)
| |
Collapse
|