Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii
Abstract Background Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interv...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Respiratory Research |
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| Online Access: | https://doi.org/10.1186/s12931-023-02624-x |
| _version_ | 1797362914210873344 |
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| author | Zhaodong Zeng Jiefang Wu Genggeng Qin Dong Yu Zilong He Weixiong Zeng Hao Zhou Jiongbin Lin Laiyu Liu Chunxia Qi Weiguo Chen |
| author_facet | Zhaodong Zeng Jiefang Wu Genggeng Qin Dong Yu Zilong He Weixiong Zeng Hao Zhou Jiongbin Lin Laiyu Liu Chunxia Qi Weiguo Chen |
| author_sort | Zhaodong Zeng |
| collection | DOAJ |
| description | Abstract Background Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. Methods We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. Results Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638–0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. Conclusion Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection. |
| first_indexed | 2024-03-08T16:14:16Z |
| format | Article |
| id | doaj.art-54d4ff93d25e4d20a86c919bbd0a99ae |
| institution | Directory Open Access Journal |
| issn | 1465-993X |
| language | English |
| last_indexed | 2024-03-08T16:14:16Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj.art-54d4ff93d25e4d20a86c919bbd0a99ae2024-01-07T12:41:14ZengBMCRespiratory Research1465-993X2024-01-0125111110.1186/s12931-023-02624-xUsing time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumanniiZhaodong Zeng0Jiefang Wu1Genggeng Qin2Dong Yu3Zilong He4Weixiong Zeng5Hao Zhou6Jiongbin Lin7Laiyu Liu8Chunxia Qi9Weiguo Chen10Department of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Respiratory and Critical Care Medicine, Chronic Airways Diseases Laboratory, Nanfang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Hospital Infection Management, ZhuJiang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityDepartment of Respiratory and Critical Care Medicine, Chronic Airways Diseases Laboratory, Nanfang Hospital of Southern Medical UniversityDepartment of Hospital Infection Management, NanFang Hospital of Southern Medical UniversityDepartment of Radiology, NanFang Hospital of Southern Medical UniversityAbstract Background Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. Methods We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. Results Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638–0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. Conclusion Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.https://doi.org/10.1186/s12931-023-02624-xAcinetobacter baumanniiMachine learningTime-series chest radiographs and laboratory dataInfection and colonization |
| spellingShingle | Zhaodong Zeng Jiefang Wu Genggeng Qin Dong Yu Zilong He Weixiong Zeng Hao Zhou Jiongbin Lin Laiyu Liu Chunxia Qi Weiguo Chen Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii Respiratory Research Acinetobacter baumannii Machine learning Time-series chest radiographs and laboratory data Infection and colonization |
| title | Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii |
| title_full | Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii |
| title_fullStr | Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii |
| title_full_unstemmed | Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii |
| title_short | Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii |
| title_sort | using time series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of acinetobacter baumannii |
| topic | Acinetobacter baumannii Machine learning Time-series chest radiographs and laboratory data Infection and colonization |
| url | https://doi.org/10.1186/s12931-023-02624-x |
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