1. Prediction of methicillin-resistant Staphylococcus aureus and carbapenem-resistant Klebsiella pneumoniae from flagged blood cultures by combining rapid Sepsityper MALDI-TOF mass spectrometry with machine learning.
- Author
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Yu, Jiaxin, Lin, Hsiu-Hsien, Tseng, Kun-Hao, Lin, Yu-Tzu, Chen, Wei-Cheng, Tien, Ni, Cho, Chia-Fong, Liang, Shinn-Jye, Ho, Lu-Ching, Hsieh, Yow-Wen, Hsu, Kai Cheng, Ho, Mao-Wang, Hsueh, Po-Ren, and Cho, Der-Yang
- Subjects
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CARBAPENEM-resistant bacteria , *METHICILLIN-resistant staphylococcus aureus , *KLEBSIELLA pneumoniae , *MACHINE learning , *MASS spectrometry - Abstract
• Combination of the Rapid Sepsityper Kit and a machine learning (ML)-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) approach for the rapid prediction of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Klebsiella pneumoniae (CRKP) from positive blood culture bottles. • In total, 47 S. aureus isolates comprising 406 MALDI-TOF MS files and 132 K. pneumoniae isolates comprising 1249 MALDI-TOF MS files were evaluated. • Accuracy, area under the curve, and F1 score for MRSA/MSSA were 0.875, 0.898 and 0.904, respectively; these values for CRKP/CSKP were 0.766, 0.828 and 0.795, respectively. • The novel ML-based MALDI-TOF MS approach enables rapid identification of MRSA and CRKP from flagged blood cultures within 1 h. This enables earlier initiation of targeted antimicrobial therapy, reducing deaths due to sepsis. This study investigated combination of the Rapid Sepsityper Kit and a machine learning (ML)-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) approach for rapid prediction of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Klebsiella pneumoniae (CRKP) from positive blood culture bottles. The study involved 461 patients with monomicrobial bloodstream infections. Species identification was performed using the conventional MALDI-TOF MS Biotyper system and the Rapid Sepsityper protocol. The data underwent preprocessing steps, and ML models were trained using preprocessed MALDI-TOF data and corresponding labels. The interpretability of the model was enhanced using SHapely Additive exPlanations values to identify significant features. In total, 44 S. aureus isolates comprising 406 MALDI-TOF MS files and 126 K. pneumoniae isolates comprising 1249 MALDI-TOF MS files were evaluated. This study demonstrated the feasibility of predicting MRSA among S. aureus and CRKP among K. pneumoniae isolates using MALDI-TOF MS and Sepsityper. Accuracy, area under the receiver operating characteristic curve, and F1 score for MRSA/methicillin-susceptible S. aureus were 0.875, 0.898 and 0.904, respectively; for CRKP/carbapenem-susceptible K. pneumoniae , these values were 0.766, 0.828 and 0.795, respectively. In conclusion, the novel ML-based MALDI-TOF MS approach enables rapid identification of MRSA and CRKP from flagged blood cultures within 1 h. This enables earlier initiation of targeted antimicrobial therapy, reducing deaths due to sepsis. The favourable performance and reduced turnaround time of this method suggest its potential as a rapid detection strategy in clinical microbiology laboratories, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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