Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model
Abstract 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 lase...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | Journal of Biomedical Science |
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| Online Access: | https://doi.org/10.1186/s12929-023-00918-2 |
| _version_ | 1827961196402180096 |
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| author | Yu-Ming Zhang Mei-Fen Tsao Ching-Yu Chang Kuan-Ting Lin Joseph Jordan Keller Hsiu-Chen Lin |
| author_facet | Yu-Ming Zhang Mei-Fen Tsao Ching-Yu Chang Kuan-Ting Lin Joseph Jordan Keller Hsiu-Chen Lin |
| author_sort | Yu-Ming Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T16:21:49Z |
| format | Article |
| id | doaj.art-a2b67a7ed307488a8727416d3f0990e4 |
| institution | Directory Open Access Journal |
| issn | 1423-0127 |
| language | English |
| last_indexed | 2024-04-09T16:21:49Z |
| publishDate | 2023-04-01 |
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| series | Journal of Biomedical Science |
| spelling | doaj.art-a2b67a7ed307488a8727416d3f0990e42023-04-23T11:24:36ZengBMCJournal of Biomedical Science1423-01272023-04-0130111010.1186/s12929-023-00918-2Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network modelYu-Ming Zhang0Mei-Fen Tsao1Ching-Yu Chang2Kuan-Ting Lin3Joseph Jordan Keller4Hsiu-Chen Lin5School of Medicine, College of Medicine, Taipei Medical UniversityDepartment of Laboratory Medicine, Taipei Medical University HospitalDepartment of Laboratory Medicine, Taipei Medical University HospitalDepartment of Business Administration, National Taiwan UniversityWestern Michigan University Homer Stryker M.D. School of Medicine, Department of PsychiatryDepartment of Clinical Pathology, Taipei Medical University HospitalAbstract 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.https://doi.org/10.1186/s12929-023-00918-2Carbapenem-resistant Klebsiella pneumoniaeMALDI-TOF MSMachine learningArtificial neural networkAntimicrobial resistanceFeature selection |
| spellingShingle | Yu-Ming Zhang Mei-Fen Tsao Ching-Yu Chang Kuan-Ting Lin Joseph Jordan Keller Hsiu-Chen Lin Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model Journal of Biomedical Science Carbapenem-resistant Klebsiella pneumoniae MALDI-TOF MS Machine learning Artificial neural network Antimicrobial resistance Feature selection |
| title | Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model |
| title_full | Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model |
| title_fullStr | Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model |
| title_full_unstemmed | Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model |
| title_short | Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model |
| title_sort | rapid identification of carbapenem resistant klebsiella pneumoniae based on matrix assisted laser desorption ionization time of flight mass spectrometry and an artificial neural network model |
| topic | Carbapenem-resistant Klebsiella pneumoniae MALDI-TOF MS Machine learning Artificial neural network Antimicrobial resistance Feature selection |
| url | https://doi.org/10.1186/s12929-023-00918-2 |
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