Rapid and Reproducible MALDI-TOF-Based Method for the Detection of Vancomycin-Resistant Enterococcus faecium Using Classifying Algorithms.
Ana CandelaManuel J ArroyoÁngela Sánchez-MolledaGema MéndezLidia QuirogaAdrián RuizEmilia CercenadoMercedes MarínPatricia MuñozLuis ManceraDavid Rodríguez-TemporalBelén Rodríguez-SánchezPublished in: Diagnostics (Basel, Switzerland) (2022)
Vancomycin-resistant Enterococcus faecium represents a health threat due to its ability to spread and cause outbreaks. MALDI-TOF MS has demonstrated its usefulness for E. faecium identification, but its implementation for antimicrobial resistance detection is still under evaluation. This study assesses the repeatability of MALDI-TOF MS for peak analysis and its performance in the discrimination of vancomycin-susceptible (VSE) from vancomycin-resistant isolates (VRE). The study was carried out on protein spectra from 178 E. faecium unique clinical isolates-92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Technical and biological repeatability were assayed. Unsupervised (principal component analysis, (PCA)) and supervised algorithms (support vector machine (SVM), random forest (RF) and partial least squares-discriminant analysis (PLS-DA)) were applied. The repeatability assay was performed with 18 peaks common to VSE and VRE with intensities above 1.0% of the maximum peak intensity. It showed lower variability for normalized data and for the peaks within the 3000-9000 m / z range. It was found that 80.9%, 79.2% and 77.5% VSE vs. VRE discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct internal differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.6% cases. The implementation of MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.
Keyphrases
- mass spectrometry
- data analysis
- machine learning
- healthcare
- antimicrobial resistance
- loop mediated isothermal amplification
- methicillin resistant staphylococcus aureus
- deep learning
- primary care
- public health
- ms ms
- climate change
- high throughput
- multiple sclerosis
- escherichia coli
- electronic health record
- high intensity
- big data
- single cell
- social media
- cystic fibrosis
- neural network
- binding protein
- artificial intelligence
- genetic diversity
- infectious diseases
- health promotion