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

Abstract

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 non-linear 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

Ethical Approval

There is no ethical concern in this study, and The Medical Ethics Committee approved this experiment at the First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from patients under the Declaration of Helsinki.

Acknowledgments

This work was financially supported by the Provincial Natural Science Research project of universities in Anhui Province (grant number: KJ2015A337).

Disclosure

The authors report no conflicts of interest in this work.