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

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

Citation: ANALYTICAL CHEMISTRY, Vol.97(29) : 15667-15675, 2025-07

Journal Title: ANALYTICAL CHEMISTRY

ISSN: 0003-2700

Issue Date: 2025-07

MeSH: 0

Keywords: Humans ; Machine Learning* ; Methicillin-Resistant Staphylococcus aureus* / drug effects ; Methicillin-Resistant Staphylococcus aureus* / isolation & purification ; Microbial Sensitivity Tests ; Software ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization* / methods ; Staphylococcal Infections / diagnosis ; Staphylococcal Infections / microbiology

Article Number: 10.1021/acs.analchem.5c01286

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.

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

Yonsei Authors: Yong, Dong Eun(용동은) https://orcid.org/0000-0002-1225-8477

URI: https://ir.ymlib.yonsei.ac.kr/handle/22282913/207491

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