Rapid Detection of Carbapenem-Resistant Klebsiella pneumoniae Using Machine Learning and MALDI-TOF MS Platform

• Published in: Infection and drug resistance Vol. 15; pp. 3703 - 3710
• Main Authors: Wang, Jinyu, Xia, Cuiping, Wu, Yue, Tian, Xin, Zhang, Ke, Wang, Zhongxin
• Format: Journal Article
• Language: English
• Published: Macclesfield Dove Medical Press Limited 01.01.2022; Taylor & Francis Ltd; Dove; Dove Medical Press
• Subjects: Accuracy; Algorithms; Antibiotics; Antimicrobial agents; Bacterial pneumonia; Classification; Data processing; Drug resistance; Feature selection; Imipenem; Ionization; Ions; Klebsiella pneumoniae; Learning algorithms; Machine learning; maldi-tof ms; Mass spectrometry; Mass spectroscopy; Original Research; Pneumonia; Scientific imaging; Software; Support vector machines; svm; svm-k; China; France
• ISSN: 1178-6973; 1178-6973
• DOI: 10.2147/IDR.S367209

Background: Rapid detection of carbapenem-resistant Klebsiella pneumoniae (CRKP) is essential for specific antimicrobial therapy. Machine learning techniques combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) can be used as a rapid, reliable, sensitive, and low-cost species identification method. Methods: Clinically collected K. pneumoniae were subjected to MALDI-TOF MS analysis. A random forest (RF) algorithm and nonlinear support vector machine (SVM) were used to construct the RF, SVM, and dimension reduction (SVM-K) models, and their performance was assessed for accuracy, sensitivity, specificity, and area under the subject worker curve (AUC). Results: The RF, SVM and SVM-K models showed good classification performance with 0.88, 0.88, and 0.91 accuracy, 0.82, 0.85, and 0.89 sensitivity, 0.93, 0.92, and 0.94 specificity with an AUC of 0.9013, 0.9298, and 0.9356, respectively. For the SVM-K model, the optimal dimension reduction was 105 to 153, and the average accuracy was >0.9. The top 10 peak features of significance according to the RF algorithm with 6515 Da appeared in 56.8% of CRKP isolates and 5.3% of CSKP isolates, which indicated the best classification performance. Conclusion: The three RF, SVM, and SVM-K models showed excellent classification performance differentiating the CRKP from CSKP; the SVM-K model was the best. Data analysis with machine learning combined with MALDI-TOF MS can be employed as a rapid and inexpensive alternative to existing detection methods. Keywords: Klebsiella pneumoniae, RF, SVM, SVM-K, MALDI-TOF MS