Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models
Abstract Background Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and t...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Microbiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12866-025-03755-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585966876884992 |
|---|---|
| author | Xiaobo Xu Zhaofeng Wang Erjie Lu Tao Lin Hengchao Du Zhongfei Li Jiahong Ma |
| author_facet | Xiaobo Xu Zhaofeng Wang Erjie Lu Tao Lin Hengchao Du Zhongfei Li Jiahong Ma |
| author_sort | Xiaobo Xu |
| collection | DOAJ |
| description | Abstract Background Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and targeted treatment and improve their survival. This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). Bacterial species were identified by MALDI-TOF MS in positive blood cultures isolated via the serum isolation gel method, and E. coli and K. pneumoniae in positive blood cultures were collected and placed into machine learning models to predict susceptibility to carbapenems. The aim of this study was to provide rapid detection of CREC and CRKP in blood cultures, to shorten the turnaround time for laboratory reporting, and to provide a basis for early clinical intervention and rational use of antibiotics. Results The collected MALDI-TOF MS data of 640 E. coli and 444 K. pneumoniae were analysed by machine learning algorithms. The area under the receiver operating characteristic curve (AUROC) for the diagnosis of E. coli susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.95, 1.00, 0.99, 0.99, and 1.00, respectively, and the accuracy in predicting 149 E. coli-positive blood cultures were 0.89, 0.92, 0.90, 0.92, and 0.86, respectively. The AUROC for the diagnosis of K. pneumoniae susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.78, 0.95, 0.93, 0.90, and 0.95, respectively, and the accuracy in predicting 127 K. pneumoniae-positive blood cultures were 0.76, 0.86, 0.81, 0.80, and 0.76, respectively. Conclusions Machine learning models constructed by MALDI-TOF MS were able to directly predict the susceptibility of E. coli and K. pneumoniae in positive blood cultures to carbapenems. This rapid identification of CREC and CRKP reduces detection time and contributes to early warning and response to potential antibiotic resistance problems in the clinic. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-14879f6bff804cfa891f94628555dd6d |
| institution | Kabale University |
| issn | 1471-2180 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Microbiology |
| spelling | doaj-art-14879f6bff804cfa891f94628555dd6d2025-01-26T12:17:54ZengBMCBMC Microbiology1471-21802025-01-012511810.1186/s12866-025-03755-5Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning modelsXiaobo Xu0Zhaofeng Wang1Erjie Lu2Tao Lin3Hengchao Du4Zhongfei Li5Jiahong Ma6Department of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalDepartment of Clinical Laboratory, Zhejiang Rong Jun HospitalAbstract Background Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and targeted treatment and improve their survival. This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). Bacterial species were identified by MALDI-TOF MS in positive blood cultures isolated via the serum isolation gel method, and E. coli and K. pneumoniae in positive blood cultures were collected and placed into machine learning models to predict susceptibility to carbapenems. The aim of this study was to provide rapid detection of CREC and CRKP in blood cultures, to shorten the turnaround time for laboratory reporting, and to provide a basis for early clinical intervention and rational use of antibiotics. Results The collected MALDI-TOF MS data of 640 E. coli and 444 K. pneumoniae were analysed by machine learning algorithms. The area under the receiver operating characteristic curve (AUROC) for the diagnosis of E. coli susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.95, 1.00, 0.99, 0.99, and 1.00, respectively, and the accuracy in predicting 149 E. coli-positive blood cultures were 0.89, 0.92, 0.90, 0.92, and 0.86, respectively. The AUROC for the diagnosis of K. pneumoniae susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.78, 0.95, 0.93, 0.90, and 0.95, respectively, and the accuracy in predicting 127 K. pneumoniae-positive blood cultures were 0.76, 0.86, 0.81, 0.80, and 0.76, respectively. Conclusions Machine learning models constructed by MALDI-TOF MS were able to directly predict the susceptibility of E. coli and K. pneumoniae in positive blood cultures to carbapenems. This rapid identification of CREC and CRKP reduces detection time and contributes to early warning and response to potential antibiotic resistance problems in the clinic. Clinical trial number Not applicable.https://doi.org/10.1186/s12866-025-03755-5Blood culturesMALDI-TOF MSMachine learningEscherichia coliKlebsiella pneumoniae |
| spellingShingle | Xiaobo Xu Zhaofeng Wang Erjie Lu Tao Lin Hengchao Du Zhongfei Li Jiahong Ma Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models BMC Microbiology Blood cultures MALDI-TOF MS Machine learning Escherichia coli Klebsiella pneumoniae |
| title | Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models |
| title_full | Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models |
| title_fullStr | Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models |
| title_full_unstemmed | Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models |
| title_short | Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models |
| title_sort | rapid detection of carbapenem resistant escherichia coli and carbapenem resistant klebsiella pneumoniae in positive blood cultures via maldi tof ms and tree based machine learning models |
| topic | Blood cultures MALDI-TOF MS Machine learning Escherichia coli Klebsiella pneumoniae |
| url | https://doi.org/10.1186/s12866-025-03755-5 |
| work_keys_str_mv | AT xiaoboxu rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT zhaofengwang rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT erjielu rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT taolin rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT hengchaodu rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT zhongfeili rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels AT jiahongma rapiddetectionofcarbapenemresistantescherichiacoliandcarbapenemresistantklebsiellapneumoniaeinpositivebloodculturesviamalditofmsandtreebasedmachinelearningmodels |