MALDI-TOF MS combined with machine learning for rapid identification of extended-spectrum β-lactamase-producing Escherichia coli
10.3760/cma.j.cn114452-20241009-00550
- VernacularTitle:MALDI-TOF MS结合机器学习快速鉴定产超广谱β内酰胺酶CTX-M型大肠埃希菌
- Author:
Rongrong DONG
1
;
Yifei WANG
;
Xinhua GUO
;
Jiayin WANG
;
Hao WANG
;
Xufeng JI
;
Qi ZHOU
;
Jiancheng XU
Author Information
1. 吉林大学第一医院检验科，长春130021
- Publication Type:Journal Article
- Keywords:
Spectrometry, mass, matrix-assisted laser desorption-ionization;
Machine learning;
Extended-spectrum beta-lactamases
- From:
Chinese Journal of Laboratory Medicine
2025;48(4):490-497
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.
