Rapid determination of colistin resistance in Klebsiella pneumoniae by MALDI-TOF peak based machine learning algorithm with MATLAB

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dc.authoridYALCIN, METIN/0000-0002-7451-4149
dc.contributor.authorIskender, Secil
dc.contributor.authorHeydarov, Saddam
dc.contributor.authorYalcin, Metin
dc.contributor.authorFaydaci, Cagri
dc.contributor.authorKurt, Ozge
dc.contributor.authorSurme, Serkan
dc.contributor.authorKucukbasmaci, Omer
dc.date.accessioned2024-09-11T19:50:52Z
dc.date.available2024-09-11T19:50:52Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractIntroduction: To date, limited data exist on demonstrating the usefulness of machine learning (ML) algorithms applied to MALDI-TOF in determining colistin resistance among Klebsiella pneumoniae. We aimed to detect colistin resistance in K. pneumoniae using MATLAB on MALDI-TOF database.Materials and methods: A total of 260 K. pneumoniae isolates were collected. Three ML models, namely, linear discriminant analysis (LDA), support vector machine, and Ensemble were used as ML algorithms and applied to training data set.Results: The accuracies for the training phase with 200 isolates were found to be 99.3%, 93.1%, and 88.3% for LDA, support vector machine, and Ensemble models, respectively. Accuracy, sensitivity, specificity, and precision values for LDA in the application test set with 60 K. pneumoniae isolates were 81.6%, 66.7%, 91.7%, and 84.2%, respectively.Conclusion: This study provides a rapid and accurate MALDI-TOF MS screening assay for clinical practice in identifying colistin resistance in K. pneumoniae.(c) 2023 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipScientific Research Proj- ects Coordination Unit of Istanbul University-Cerrahpasa [33529]en_US
dc.description.sponsorshipThis study was financially supported by Scientific Research Proj- ects Coordination Unit of Istanbul University-Cerrahpasa (project number: 33529) . No writing assistance was utilized in the production of this manuscript.en_US
dc.identifier.doi10.1016/j.diagmicrobio.2023.116052
dc.identifier.issn0732-8893
dc.identifier.issn1879-0070
dc.identifier.issue4en_US
dc.identifier.pmid37769565en_US
dc.identifier.scopus2-s2.0-85172279475en_US
dc.identifier.urihttps://doi.org/10.1016/j.diagmicrobio.2023.116052
dc.identifier.urihttps://hdl.handle.net/11363/7691
dc.identifier.volume107en_US
dc.identifier.wosWOS:001084143100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofDiagnostic Microbiology And Infectious Diseaseen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectColistin resistanceen_US
dc.subjectKlebsiella pneumoniaeen_US
dc.subjectMALDI-TOFen_US
dc.subjectMATLABen_US
dc.subjectMachine learningen_US
dc.titleRapid determination of colistin resistance in Klebsiella pneumoniae by MALDI-TOF peak based machine learning algorithm with MATLABen_US
dc.typeArticleen_US

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