Rapid Screening of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning: A Randomized, Multicenter Study

Dongeun Yong; Jeong Su Park; Kyungnam Kim; Donggun Seo; Dong-Chan Kim; Jae-Seok Kim; Jong-Min Park

doi:Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of healthcare-associated infections including bacteremia. The rapid detection of MRSA is essential for prompt treatment and improved outcomes. However, traditional MRSA screening and confirmatory tests based on bacterial cultures with antimicrobial susceptibility tests and/or molecular diagnostics are time-consuming (>2 days), labor-intensive, and costly. We report that AMRQuest software, which was developed using logistic regression-based machine learning and matrix-assisted laser desorption/ionization-time-of-flight spectra of S. aureus isolates, can be successfully implemented in clinical microbiology laboratories to screen MRSA and identify bacterial species simultaneously, with the cefoxitin disk diffusion test as a reference. Analytical sensitivity, specificity, percent agreement, and Cohen's kappa values were calculated to determine the accuracy of the AMRQuest software. The minimum sample size of the testing set for statistical analysis was determined considering the local prevalence of MRSA infections. MRSA screening was performed using 537 consecutive S. aureus isolates, including 231 MRSA and 306 methicillin-susceptible S. aureus isolates, from three tertiary-care hospitals. The results from the AMRQuest software were similar to those obtained using the reference method, cefoxitin disk diffusion testing, making it a powerful method for the rapid detection of MRSA prior to traditional antibiotic resistance testing.

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Rapid Screening of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning: A Randomized, Multicenter Study

DC Field	Value	Language
dc.contributor.author	용동은	-
dc.date.accessioned	2025-10-15T01:51:32Z	-
dc.date.available	2025-10-15T01:51:32Z	-
dc.date.issued	2025-07	-
dc.identifier.issn	0003-2700	-
dc.identifier.uri	https://ir.ymlib.yonsei.ac.kr/handle/22282913/207491	-
dc.description.statementOfResponsibility	T202506116.pdf	-
dc.language	English	-
dc.publisher	American Chemical Society	-
dc.relation.isPartOf	ANALYTICAL CHEMISTRY	-
dc.rights	CC BY-NC-ND 2.0 KR	-
dc.subject.MESH	0	-
dc.title	Rapid Screening of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning: A Randomized, Multicenter Study	-
dc.type	Article	-
dc.contributor.college	College of Medicine (의과대학)	-
dc.contributor.department	Dept. of Laboratory Medicine (진단검사의학교실)	-
dc.contributor.googleauthor	Dongeun Yong	-
dc.contributor.googleauthor	Jeong Su Park	-
dc.contributor.googleauthor	Kyungnam Kim	-
dc.contributor.googleauthor	Donggun Seo	-
dc.contributor.googleauthor	Dong-Chan Kim	-
dc.contributor.googleauthor	Jae-Seok Kim	-
dc.contributor.googleauthor	Jong-Min Park	-
dc.identifier.doi	Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of healthcare-associated infections including bacteremia. The rapid detection of MRSA is essential for prompt treatment and improved outcomes. However, traditional MRSA screening and confirmatory tests based on bacterial cultures with antimicrobial susceptibility tests and/or molecular diagnostics are time-consuming (>2 days), labor-intensive, and costly. We report that AMRQuest software, which was developed using logistic regression-based machine learning and matrix-assisted laser desorption/ionization-time-of-flight spectra of S. aureus isolates, can be successfully implemented in clinical microbiology laboratories to screen MRSA and identify bacterial species simultaneously, with the cefoxitin disk diffusion test as a reference. Analytical sensitivity, specificity, percent agreement, and Cohen's kappa values were calculated to determine the accuracy of the AMRQuest software. The minimum sample size of the testing set for statistical analysis was determined considering the local prevalence of MRSA infections. MRSA screening was performed using 537 consecutive S. aureus isolates, including 231 MRSA and 306 methicillin-susceptible S. aureus isolates, from three tertiary-care hospitals. The results from the AMRQuest software were similar to those obtained using the reference method, cefoxitin disk diffusion testing, making it a powerful method for the rapid detection of MRSA prior to traditional antibiotic resistance testing.	-
dc.contributor.localId	A02423	-
dc.relation.journalcode	J00136	-
dc.identifier.eissn	1520-6882	-
dc.identifier.pmid	40665608	-
dc.subject.keyword	Humans	-
dc.subject.keyword	Machine Learning*	-
dc.subject.keyword	Methicillin-Resistant Staphylococcus aureus* / drug effects	-
dc.subject.keyword	Methicillin-Resistant Staphylococcus aureus* / isolation & purification	-
dc.subject.keyword	Microbial Sensitivity Tests	-
dc.subject.keyword	Software	-
dc.subject.keyword	Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization* / methods	-
dc.subject.keyword	Staphylococcal Infections / diagnosis	-
dc.subject.keyword	Staphylococcal Infections / microbiology	-
dc.contributor.alternativeName	Yong, Dong Eun	-
dc.contributor.affiliatedAuthor	용동은	-
dc.citation.volume	97	-
dc.citation.number	29	-
dc.citation.startPage	15667	-
dc.citation.endPage	15675	-
dc.identifier.bibliographicCitation	ANALYTICAL CHEMISTRY, Vol.97(29) : 15667-15675, 2025-07	-
dc.identifier.articleno	10.1021/acs.analchem.5c01286	-

Appears in Collections:: 1. College of Medicine (의과대학) > Dept. of Laboratory Medicine (진단검사의학교실) > 1. Journal Papers

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