Cargando…

Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods

Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for K...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Chunxuan, Wang, Zhuo, Wang, Hsin-Yao, Chung, Chia-Ru, Horng, Jorng-Tzong, Lu, Jang-Jih, Lee, Tzong-Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959214/
https://www.ncbi.nlm.nih.gov/pubmed/35356528
http://dx.doi.org/10.3389/fmicb.2022.827451
_version_ 1784677101093257216
author Wang, Chunxuan
Wang, Zhuo
Wang, Hsin-Yao
Chung, Chia-Ru
Horng, Jorng-Tzong
Lu, Jang-Jih
Lee, Tzong-Yi
author_facet Wang, Chunxuan
Wang, Zhuo
Wang, Hsin-Yao
Chung, Chia-Ru
Horng, Jorng-Tzong
Lu, Jang-Jih
Lee, Tzong-Yi
author_sort Wang, Chunxuan
collection PubMed
description Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability.
format Online
Article
Text
id pubmed-8959214
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89592142022-03-29 Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods Wang, Chunxuan Wang, Zhuo Wang, Hsin-Yao Chung, Chia-Ru Horng, Jorng-Tzong Lu, Jang-Jih Lee, Tzong-Yi Front Microbiol Microbiology Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability. Frontiers Media S.A. 2022-03-08 /pmc/articles/PMC8959214/ /pubmed/35356528 http://dx.doi.org/10.3389/fmicb.2022.827451 Text en Copyright © 2022 Wang, Wang, Wang, Chung, Horng, Lu and Lee. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Wang, Chunxuan
Wang, Zhuo
Wang, Hsin-Yao
Chung, Chia-Ru
Horng, Jorng-Tzong
Lu, Jang-Jih
Lee, Tzong-Yi
Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title_full Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title_fullStr Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title_full_unstemmed Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title_short Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods
title_sort large-scale samples based rapid detection of ciprofloxacin resistance in klebsiella pneumoniae using machine learning methods
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959214/
https://www.ncbi.nlm.nih.gov/pubmed/35356528
http://dx.doi.org/10.3389/fmicb.2022.827451
work_keys_str_mv AT wangchunxuan largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT wangzhuo largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT wanghsinyao largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT chungchiaru largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT horngjorngtzong largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT lujangjih largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods
AT leetzongyi largescalesamplesbasedrapiddetectionofciprofloxacinresistanceinklebsiellapneumoniaeusingmachinelearningmethods