{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:14:21Z","timestamp":1774894461155,"version":"3.50.1"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2018-67030-28361"],"award-info":[{"award-number":["2018-67030-28361"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to <jats:italic>Salmonella<\/jats:italic> heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid &amp; Cephalothin and joint resistance to Ampicillin &amp; Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.<\/jats:p>","DOI":"10.1371\/journal.pone.0261528","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T18:27:50Z","timestamp":1640802470000},"page":"e0261528","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Bayesian approach to modeling antimicrobial multidrug resistance"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4745-4662","authenticated-orcid":true,"given":"Min","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4489-4344","authenticated-orcid":true,"given":"Chong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Annette","family":"O\u2019Connor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"pone.0261528.ref001","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1146\/annurev.biochem.78.082907.145923","article-title":"Multidrug resistance in bacteria","volume":"78","author":"H Nikaido","year":"2009","journal-title":"Annual Review of Biochemistry"},{"issue":"3","key":"pone.0261528.ref002","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1586\/eri.13.4","article-title":"Impact of antimicrobial multidrug resistance on inpatient care cost: an evaluation of the evidence","volume":"11","author":"GS Tansarli","year":"2013","journal-title":"Expert Review of Anti-infective Therapy"},{"issue":"6","key":"pone.0261528.ref003","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.ijantimicag.2005.12.013","article-title":"Multidrug resistance to antimicrobials as a predominant factor influencing patient survival","volume":"27","author":"EJ Giamarellos-Bourboulis","year":"2006","journal-title":"International Journal of Antimicrobial Agents"},{"issue":"10","key":"pone.0261528.ref004","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1089\/fpd.2017.2283","article-title":"National antimicrobial resistance monitoring system: two decades of advancing public health through integrated surveillance of antimicrobial resistance","volume":"14","author":"BE Karp","year":"2017","journal-title":"Foodborne Pathogens and Disease"},{"issue":"2","key":"pone.0261528.ref005","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nprot.2007.521","article-title":"Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances","volume":"3","author":"I Wiegand","year":"2008","journal-title":"Nature Protocols"},{"issue":"2","key":"pone.0261528.ref006","doi-asserted-by":"crossref","DOI":"10.1128\/JCM.01260-18","article-title":"Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella","volume":"57","author":"M Nguyen","year":"2019","journal-title":"Journal of Clinical Microbiology"},{"issue":"1","key":"pone.0261528.ref007","first-page":"1","article-title":"Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae","volume":"8","author":"M Nguyen","year":"2018","journal-title":"Scientific reports"},{"issue":"7","key":"pone.0261528.ref008","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1093\/jac\/dkx067","article-title":"WGS to predict antibiotic MICs for Neisseria gonorrhoeae","volume":"72","author":"DW Eyre","year":"2017","journal-title":"Journal of Antimicrobial Chemotherapy"},{"issue":"3","key":"pone.0261528.ref009","first-page":"e00756","article-title":"Penicillin-binding protein transpeptidase signatures for tracking and predicting \u03b2-lactam resistance levels in Streptococcus pneumoniae","volume":"7","author":"Y Li","year":"2016","journal-title":"MBio"},{"key":"pone.0261528.ref010","unstructured":"CDC. 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