- 作者: Yu-Ming Zhang, Mei-Fen Tsao, Ching-Yu Chang, Kuan-Ting Lin, Joseph Jordan Keller & Hsiu-Chen Lin
- 作者服務機構: 1.Department of Business Administration, National Taiwan University, Taipei, Taiwan 2.Department of Clinical Pathology, Taipei Medical University Hospital, Taipei, Taiwan 3.Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei, Taiwan 4.Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wu-Hsing St, Taipei, 11031, Taiwan 5.School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 6.Western Michigan University Homer Stryker M.D. School of Medicine, Department of Psychiatry, Kalamazoo, MI, USA
- 中文摘要:
- 英文摘要:
Background
Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens.
Methods
A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously.
Results
Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision–recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da.
Conclusions
Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients’ outcomes and lower the prevalence of antimicrobial resistance. - 中文關鍵字:
- 英文關鍵字: Carbapenem-resistant Klebsiella pneumoniae, MALDI-TOF MS, Machine learning, Artifcial neural network, Antimicrobial resistance, Feature selection