Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin

Microbiology Spectrum

Chia-Ru Chung¹  Yi-Ju Tseng²  Jorng-Tzong Horng²  Min-Hsien Wu²  Tsui-Ping Liu²  Jang-Jih Lu²  Hsin-Yao Wang²  Ting-Wei Lin²  Wan-Ting Huang³  Tzu-Hao Chang⁴  Chao-Jung Chen⁵  Ko-Pei Lu⁶  Tzong-Yi Lee⁷ 

[1] Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan;Department of Information Management, National Central University, Taoyuan City, Taiwan;Department of Pathology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan;Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei City, Taiwan;Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan;Graduate Program in Biomedical Information, Yuan-Ze University, Taoyuan City, Taiwan;School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China;

关键词: vancomycin-resistant Enterococcus faecium;    antibacterial drug resistance;    matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry;    machine learning;    rapid detection;    Enterococcus faecium;   

DOI  :  10.1128/Spectrum.00913-21

来源: DOAJ

【 摘 要 】

ABSTRACT Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

【 授权许可】

Unknown