{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:44:35Z","timestamp":1790959475915,"version":"4.1.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Vancomycin-resistant\n                      <jats:italic>Enterococcus faecium<\/jats:italic>\n                      (VRE\n                      <jats:italic>fm<\/jats:italic>\n                      ) is a World Health Organization priority pathogen, yet conventional phenotypic susceptibility testing requires up to 72\u202fh, delaying targeted antimicrobial therapy. This study aimed to develop an interpretable and rapid machine learning classifier to predict VRE\n                      <jats:italic>fm<\/jats:italic>\n                      using matrix-assisted laser desorption\/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectra.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively analyzed 268 clinical\n                      <jats:italic>E. faecium<\/jats:italic>\n                      isolates (100 VRE\n                      <jats:italic>fm<\/jats:italic>\n                      and 168 vancomycin-susceptible\n                      <jats:italic>E. faecium<\/jats:italic>\n                      ) from Tangshan Gongren Hospital, a tertiary hospital in northern China (2020\u20132025). Spectra were preprocessed with smoothing, baseline removal, and 5-Da binning (range 2,000\u201320,000 Da). Extreme gradient boosting recursive feature elimination selected 40 discriminative mass-to-charge ratio features. To enhance model generalizability across diverse bacterial lineages, the training set combined local isolates from 2020 to 2024 with the multi-center DRIAMS repository (subsets A\u2013D). Five classifiers were trained under stratified 5-fold cross-validation and temporally validated on the independent 2025 local isolates. Model discrimination, calibration, and clinical utility were evaluated using the area under the receiver operating characteristic curve (AUROC), Brier score, and decision curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The k-nearest neighbors classifier achieved optimal temporal validation performance (area under the receiver operating characteristic curve 0.90, F1 score 0.683, Brier score 0.185). Hybrid training configurations combining local data with pooled DRIAMS subsets retained clinically useful discrimination (AUROC &amp;gt; 0.80), whereas external-only models performed at chance level (AUROC \u2248 0.50).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      MALDI-TOF MS combined with machine learning enables rapid, interpretable prediction of vancomycin resistance in\n                      <jats:italic>E. faecium<\/jats:italic>\n                      . A hybrid local\u2013multi-center training strategy offers a pragmatic solution for laboratories with limited local sample availability, facilitating clinical deployment of spectral-based antimicrobial resistance surveillance.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1789841","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:37:34Z","timestamp":1781588254000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Rapid prediction of vancomycin-resistant Enterococcus faecium using MALDI-TOF mass spectrometry and machine learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanrui","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Tangshan Gongren Hospital","place":["Tangshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Foshan Fosun Chancheng Hospital","place":["Foshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiping","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Tangshan Gongren Hospital","place":["Tangshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Tangshan Nanhu Hospital","place":["Tangshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Tangshan Gongren Hospital","place":["Tangshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Laoting County Hospital","place":["Tangshan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congcong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Hebei Provincial Hospital of Chinese Medicine","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s13205-012-0074-4","article-title":"Molecular techniques for detecting and typing of bacteria, advantages and application to foodborne pathogens isolated from ducks","volume":"3","author":"Adzitey","year":"2013","journal-title":"3 Biotech"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.2174\/1568005023342506","article-title":"Antibiotics targeting ribosomes: crystallographic studies","volume":"2","author":"Auerbach","year":"2002","journal-title":"Curr. Drug Targets Infect. Disord."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e0284315","DOI":"10.1371\/journal.pone.0284315","article-title":"Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification","volume":"18","author":"Bifarin","year":"2023","journal-title":"PLoS One"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1128\/jcm.02697-14","article-title":"Performance of Vitek 2 for antimicrobial susceptibility testing of Enterobacteriaceae with Vitek 2 (2009 FDA) and 2014 CLSI breakpoints","volume":"53","author":"Bobenchik","year":"2015","journal-title":"J. Clin. Microbiol."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.cmi.2019.10.027","article-title":"Antimicrobial resistance classification using MALDI-TOF-MS is not that easy: lessons from vancomycin-resistant Enterococcus faecium","volume":"26","author":"Brackmann","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e0254722","DOI":"10.1128\/spectrum.02547-22","article-title":"Multicenter evaluation of the FilmArray blood culture identification 2 panel for pathogen detection in bloodstream infections","volume":"11","author":"Cam\u00e9l\u00e9na","year":"2023","journal-title":"Microbiol. Spectrum"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.1128\/jcm.00267-17","article-title":"Rapid differentiation of Haemophilus influenzae and Haemophilus haemolyticus by use of matrix-assisted laser desorption ionization-time of flight mass spectrometry with ClinProTools mass spectrum analysis","volume":"55","author":"Chen","year":"2017","journal-title":"J. Clin. Microbiol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ymeth.2020.05.024","article-title":"Interpretable machine learning in bioinformatics","volume":"179","author":"Cho","year":"2020","journal-title":"Methods"},{"key":"ref9","volume-title":"Performance Standards for Antimicrobial Susceptibility Testing; M100","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378.10.1136\/bmj-2023-","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ajic.2013.11.010","article-title":"Identification of temporal clusters and risk factors of bacteremia by nosocomial vancomycin-resistant Enterococci","volume":"42","author":"da Silva","year":"2014","journal-title":"Am. J. Infect. Control"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.clinms.2019.05.003","article-title":"MALDI-MS: emerging roles in pathology and laboratory medicine","volume":"13","author":"Duncan","year":"2019","journal-title":"Clin. Mass Spectrom."},{"key":"ref13","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1128\/microbiolspec.GPP3-0053-2018","article-title":"Pathogenicity of enterococci","volume":"7","author":"Fiore","year":"2019","journal-title":"Microbiol. Spectr."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"3558","DOI":"10.1128\/aac.00283-08","article-title":"Peptide nucleic acid fluorescent in situ hybridization for hospital-acquired enterococcal bacteremia: delivering earlier effective antimicrobial therapy","volume":"52","author":"Forrest","year":"2008","journal-title":"Antimicrob. Agents Chemother."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/antibiotics11040427","article-title":"Antimicrobial susceptibility testing: a comprehensive review of currently used methods","volume":"11","author":"Gajic","year":"2022","journal-title":"Antibiotics (Basel)"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"923","DOI":"10.3390\/microorganisms9050923","article-title":"Antimicrobial resistance gene detection methods for bacteria in animal-based foods: a brief review of highlights and advantages","volume":"9","author":"Galhano","year":"2021","journal-title":"Microorganisms"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"13398","DOI":"10.1021\/acs.analchem.4c00741","article-title":"Machine learning assisted MALDI mass spectrometry for rapid antimicrobial resistance prediction in clinicals","volume":"96","author":"Gao","year":"2024","journal-title":"Anal. Chem."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1128\/cmr.00058-18","article-title":"The Enterococcus: a model of adaptability to its environment","volume":"32","author":"Garc\u00eda-Solache","year":"2019","journal-title":"Clin. Microbiol. Rev."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1093\/bioinformatics\/bts447","article-title":"MALDIquant: a versatile R package for the analysis of mass spectrometry data","volume":"28","author":"Gibb","year":"2012","journal-title":"Bioinformatics"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.5578\/mb.68810","article-title":"Investigation of antibiotic resistance and virulence factors of Enterococcus faecium and Enterococcus faecalis strains isolated from clinical samples","volume":"54","author":"G\u00f6k","year":"2020","journal-title":"Mikrobiyol. Bul."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1128\/jcm.01000-12","article-title":"Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry to identify vancomycin-resistant enterococci and investigate the epidemiology of an outbreak","volume":"50","author":"Griffin","year":"2012","journal-title":"J. Clin. Microbiol."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1128\/jcm.03544-13","article-title":"Performance of the EUCAST disk diffusion method, the CLSI agar screen method, and the Vitek 2 automated antimicrobial susceptibility testing system for detection of clinical isolates of enterococci with low- and medium-level VanB-type vancomycin resistance: a multicenter study","volume":"52","author":"Hegstad","year":"2014","journal-title":"J. Clin. Microbiol."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1128\/cmr.00058-12","article-title":"Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry for detection of antibiotic resistance mechanisms: from research to routine diagnosis","volume":"26","author":"Hrab\u00e1k","year":"2013","journal-title":"Clin. Microbiol. Rev."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1017\/ice.2019.15","article-title":"A retrospective cohort study of antibiotic exposure and vancomycin-resistant Enterococcus recolonization","volume":"40","author":"Hughes","year":"2019","journal-title":"Infect. Control Hosp. Epidemiol."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e0108025","DOI":"10.1128\/spectrum.01080-25","article-title":"Evaluation of sample pooling using Xpert Carba-R and Xpert vanA\/vanB PCR for screening of carbapenemase-producing Enterobacterales and vancomycin-resistant Enterococcus colonization","volume":"13","author":"Keidar-Friedman","year":"2025","journal-title":"Microbiol. Spectrum"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"214","DOI":"10.3390\/pathogens8040214","article-title":"Rapid discrimination of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS","volume":"8","author":"Kim","year":"2019","journal-title":"Pathogens"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ijmm.2015.04.004","article-title":"MALDI-TOF mass spectrometry following short incubation on a solid medium is a valuable tool for rapid pathogen identification from positive blood cultures","volume":"305","author":"Kohlmann","year":"2015","journal-title":"Int. J. Med. Microbiol."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-026-02370-6","article-title":"A causal and interpretable machine learning framework for postcranioplasty risk prediction and surgical decision support","volume":"9","author":"Li","year":"2026","journal-title":"NPJ Digit. Med."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.3390\/ijms26031140","article-title":"Integrating machine learning with MALDI-TOF mass spectrometry for rapid and accurate antimicrobial resistance detection in clinical pathogens","volume":"26","author":"L\u00f3pez-Cort\u00e9s","year":"2025","journal-title":"Int. J. Mol. Sci."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1128\/jcm.02428-15","article-title":"Rapid detection of vancomycin-intermediate Staphylococcus aureus by matrix-assisted laser desorption ionization-time of flight mass spectrometry","volume":"54","author":"Mather","year":"2016","journal-title":"J. Clin. Microbiol."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ijantimicag.2014.05.006","article-title":"Differentiation of vanA-positive Enterococcus faecium from vanA-negative E. faecium by matrix-assisted laser desorption\/ionisation time-of-flight mass spectrometry","volume":"44","author":"Nakano","year":"2014","journal-title":"Int. J. Antimicrob. Agents"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e1313","DOI":"10.1002\/mbo3.1313","article-title":"Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning","volume":"11","author":"Pizzato","year":"2022","journal-title":"Microbiologyopen"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13073-021-00930-x","article-title":"DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data","volume":"13","author":"Poirion","year":"2021","journal-title":"Genome Med."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e41","DOI":"10.1017\/ash.2021.215","article-title":"Impact of a vancomycin-resistant Enterococcus (VRE) screening result on appropriateness of antibiotic therapy","volume":"1","author":"Reynolds","year":"2021","journal-title":"Antimicrob. Steward. Healthc. Epidemiol."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1016\/s1473-3099(25)00118-5","article-title":"The WHO bacterial priority pathogens list 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance","volume":"25","author":"Sati","year":"2025","journal-title":"Lancet Infect. Dis."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.micpath.2016.05.019","article-title":"Molecular epidemiology of vancomycin resistant enterococci in a tertiary care hospital in Saudi Arabia","volume":"97","author":"Somily","year":"2016","journal-title":"Microb. Pathog."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1093\/milmed\/usaa229","article-title":"Epidemiology and outcomes of vancomycin-resistant Enterococcus infections in the U.S. military health system","volume":"186","author":"Stagliano","year":"2021","journal-title":"Mil. Med."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jclinepi.2019.09.016","article-title":"Validation of clinical prediction models: what does the \u201ccalibration slope\u201d really measure?","volume":"118","author":"Stevens","year":"2020","journal-title":"J. Clin. Epidemiol."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1111\/j.1469-0691.2006.01631.x","article-title":"Ecological replacement of Enterococcus faecalis by multiresistant clonal complex 17 Enterococcus faecium","volume":"13","author":"Top","year":"2007","journal-title":"Clin. Microbiol. Infect."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"38","DOI":"10.4103\/0255-0857.174099","article-title":"Prevalence, outcome and risk factor associated with vancomycin-resistant Enterococcus faecalis and Enterococcus faecium at a tertiary care hospital in northern India","volume":"34","author":"Tripathi","year":"2016","journal-title":"Indian J. Med. Microbiol."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989x06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e0091321","DOI":"10.1128\/Spectrum.00913-21","article-title":"Clinically applicable system for rapidly predicting Enterococcus faecium susceptibility to vancomycin","volume":"9","author":"Wang","year":"2021","journal-title":"Microbiol. Spectrum"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"821233","DOI":"10.3389\/fmicb.2022.821233","article-title":"Efficiently predicting vancomycin resistance of Enterococcus faecium from MALDI-TOF MS spectra using a deep learning-based approach","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Microbiol."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1255\/ejms.1298","article-title":"Application of matrix-assisted laser desorption ionization time-of-flight mass spectrometry in the screening of vanA-positive Enterococcus faecium","volume":"20","author":"Wang","year":"2014","journal-title":"Eur. J. Mass Spectrom. (Chichester)"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1038\/s41591-021-01619-9","article-title":"Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning","volume":"28","author":"Weis","year":"2022","journal-title":"Nat. Med."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"e0118625","DOI":"10.1128\/jcm.01186-25","article-title":"Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study","volume":"63","author":"Wiesmann","year":"2025","journal-title":"J. Clin. Microbiol."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1086\/421946","article-title":"Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study","volume":"39","author":"Wisplinghoff","year":"2004","journal-title":"Clin. Infect. Dis."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"3038","DOI":"10.1128\/jcm.01265-14","article-title":"Novel strategy for typing Mycoplasma pneumoniae isolates by use of matrix-assisted laser desorption ionization-time of flight mass spectrometry coupled with ClinProTools","volume":"52","author":"Xiao","year":"2014","journal-title":"J. Clin. Microbiol."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"4643","DOI":"10.1128\/aac.01710-09","article-title":"vanM, a new glycopeptide resistance gene cluster found in Enterococcus faecium","volume":"54","author":"Xu","year":"2010","journal-title":"Antimicrob. 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