{"title":"Rapid Identification and Typing of Carbapenem-Resistant Klebsiella pneumoniae Using MALDI-TOF MS and Machine Learning","authors":"Zhiyi Ye, Jin Zhu, Yang Liu, Jun Lu","doi":"10.1111/1751-7915.70184","DOIUrl":null,"url":null,"abstract":"<p>Use matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to screen the specific mass peaks of carbapenem-resistant <i>Klebsiella pneumoniae</i> (CRKP), compare the differences in spectrum peaks between intestinal and bloodstream screening of CRKP, and assess the utility of MALDI-TOF MS in quickly identifying various CRKP sources. From 2014 to 2023, a total of 267 <i>Klebsiella pneumoniae</i> strains were collected at Quzhou People's Hospital, including 60 intestinal screening isolates from ICU patients and 207 bloodstream infection isolates. MALDI-TOF MS was used to profile peptides in CRKP and carbapenem-sensitive <i>Klebsiella pneumoniae</i> (CSKP), followed by analysis with flexAnalysis and ClinProTools 3.0. Statistically significant protein peaks were selected to build classification models, which were verified using non-duplicate strains. MALDI-TOF MS achieved > 99.9% accuracy in identifying <i>Klebsiella pneumoniae</i>. Characteristic peaks (2523.43, 3041.62, 4520.11, 10,079.18 Da) were used to develop resistance analysis models, with the optimal model (SNN) showing 90.08% sensitivity, 95.80% specificity and identification accuracies of 90% for CSKP and 89.66% for CRKP. Another model using peaks (8876, 8993, 9139 Da) differentiated CRKP origins, with the ideal model (QC) achieving 86.85% sensitivity, 88.46% specificity, and accuracies of 81.82% for bloodstream and 95.00% for intestinal CRKP.</p>","PeriodicalId":209,"journal":{"name":"Microbial Biotechnology","volume":"18 6","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1751-7915.70184","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbial Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.70184","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Use matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to screen the specific mass peaks of carbapenem-resistant Klebsiella pneumoniae (CRKP), compare the differences in spectrum peaks between intestinal and bloodstream screening of CRKP, and assess the utility of MALDI-TOF MS in quickly identifying various CRKP sources. From 2014 to 2023, a total of 267 Klebsiella pneumoniae strains were collected at Quzhou People's Hospital, including 60 intestinal screening isolates from ICU patients and 207 bloodstream infection isolates. MALDI-TOF MS was used to profile peptides in CRKP and carbapenem-sensitive Klebsiella pneumoniae (CSKP), followed by analysis with flexAnalysis and ClinProTools 3.0. Statistically significant protein peaks were selected to build classification models, which were verified using non-duplicate strains. MALDI-TOF MS achieved > 99.9% accuracy in identifying Klebsiella pneumoniae. Characteristic peaks (2523.43, 3041.62, 4520.11, 10,079.18 Da) were used to develop resistance analysis models, with the optimal model (SNN) showing 90.08% sensitivity, 95.80% specificity and identification accuracies of 90% for CSKP and 89.66% for CRKP. Another model using peaks (8876, 8993, 9139 Da) differentiated CRKP origins, with the ideal model (QC) achieving 86.85% sensitivity, 88.46% specificity, and accuracies of 81.82% for bloodstream and 95.00% for intestinal CRKP.
期刊介绍:
Microbial Biotechnology publishes papers of original research reporting significant advances in any aspect of microbial applications, including, but not limited to biotechnologies related to: Green chemistry; Primary metabolites; Food, beverages and supplements; Secondary metabolites and natural products; Pharmaceuticals; Diagnostics; Agriculture; Bioenergy; Biomining, including oil recovery and processing; Bioremediation; Biopolymers, biomaterials; Bionanotechnology; Biosurfactants and bioemulsifiers; Compatible solutes and bioprotectants; Biosensors, monitoring systems, quantitative microbial risk assessment; Technology development; Protein engineering; Functional genomics; Metabolic engineering; Metabolic design; Systems analysis, modelling; Process engineering; Biologically-based analytical methods; Microbially-based strategies in public health; Microbially-based strategies to influence global processes