ArticleJournal of clinical microbiology2016
Rapid Detection of Vancomycin-Intermediate Staphylococcus aureus by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.
Article in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Application of machine learning with MALDI-TOF MS for rapid differentiation between methicillin-susceptible and methicillin-resistant Staphylococcus aureus.PLoS computational biology · 2026Article
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- Accelerated detection ofMicrobiology spectrum · 2026Article
- Rapid prediction of vancomycin-resistantFrontiers in microbiology · 2026Article
- Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.Scientific reports · 2025Article
- Genomic and phenotypic adaptations of methicillin resistant Staphylococcus aureus during vancomycin therapy.Scientific reports · 2025Article
- Frontiers in superbug management: innovating approaches to combat antimicrobial resistance.Archives of microbiology · 2025Review
- Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance.npj antimicrobials and resistance · 2025Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning.Frontiers in microbiology · 2024Article
- Differentiation betweenInternational journal of molecular sciences · 2023Article
- Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model.Journal of biomedical science · 2023Article
- Rapid and Accurate Discrimination ofBiomedicines · 2022Article
- Rapid and Simple Approaches for Diagnosis ofPolish journal of microbiology · 2022Review
- Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related SpeciesMicroorganisms · 2022Article
- MALDI-TOF MS Using a Custom-Made Database, Biomarker Assignment, or Mathematical Classifiers Does Not DifferentiateMicroorganisms · 2022Article
- Rapid Detection of Carbapenem-ResistantInfection and drug resistance · 2022Article
- MALDI-TOF Mass Spectrometry Technology as a Tool for the Rapid Diagnosis of Antimicrobial Resistance in Bacteria.Antibiotics (Basel, Switzerland) · 2021Review
- Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vancomycin is the standard of care for the treatment of invasive methicillin-resistantStaphylococcus aureus(MRSA) infections. Infections with vancomycin-nonsusceptible MRSA, including vancomycin-intermediateS. aureus(VISA) and heterogeneous VISA (hVISA), are clinically challenging and are associated with poor patient outcomes. The identification of VISA in the clinical laboratory depends on standard susceptibility testing, which takes at least 24 h to complete after isolate subculture, whereas hVISA is not routinely detected in clinical labs. We therefore sought to determine whether VISA and hVISA can be differentiated from vancomycin-susceptibleS. aureus(VSSA) using the spectra produced by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Strains of MRSA were characterized for vancomycin susceptibility phenotype by broth microdilution and modified population analysis. We tested 21 VISA, 21 hVISA, and 38 VSSA isolates by MALDI-TOF MS. Susceptibility phenotypes were separated by using a support vector machine (SVM) machine learning algorithm. The resulting model was validated by leave-one-out cross validation. Models were developed and validated by using spectral profiles generated under various subculture conditions, as well as with and without hVISA strains. Using SVM, we correctly identified 100% of the VISA and 97% of the VSSA isolates with an overall classification accuracy of 98%. Addition of hVISA to the model resulted in 76% hVISA identification, 100% VISA identification, and 89% VSSA identification, for an overall classification accuracy of 89%. We conclude that VISA/hVISA and VSSA isolates are separable by MALDI-TOF MS with SVM analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.