In:
Microbiology Spectrum, American Society for Microbiology, Vol. 9, No. 3 ( 2021-12-22)
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 (VRE fm ) and vancomycin-susceptible E. faecium (VSE fm ) strains. A predictive model was developed and validated to distinguish VRE fm and VSE fm 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 VRE fm and 2,922 VSE fm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VRE fm and 1,058 VSE fm 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 VRE fm strains from VSE fm 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 VRE fm and VSE fm isolates, including 2,795 VRE fm and 2,922 VSE fm 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 VRE fm strains from VSE fm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.
Type of Medium:
Online Resource
ISSN:
2165-0497
DOI:
10.1128/Spectrum.00913-21
Language:
English
Publisher:
American Society for Microbiology
Publication Date:
2021
detail.hit.zdb_id:
2807133-5
