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

Pathogens

卷:11

Ting-Chia Lin¹  Chien-Feng Li²  Yow-Ling Shiue³  Po-Hsin Kong³  Li-Ching Wu³  Hung-Yi Chiou⁴  Chao A. Hsiung⁴  Cheng-Hsiung Chiang⁴  Hsiao-Hui Tsou⁴  Shu-Chen Kuo⁵ 

[1] Center for Precision Medicine, Chi Mei Medical Center, Tainan 71004, Taiwan;

[2] Department of Medical Research, Chi Mei Medical Center, Tainan 71004, Taiwan;

[3] Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan;

[4] Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan;

[5] National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan;

关键词: methicillin-resistant Staphylococcus aureus;    Staphylococcus aureus bacteremia;    antimicrobial susceptibility testing;    MALDI-TOF MS;    machine learning;    binning method;   

DOI  :  10.3390/pathogens11050586

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown