Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study
Background: Ventilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked. Methods: This study aime...
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| Format: | Article |
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Elsevier
2025-02-01
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| Series: | Journal of Microbiology, Immunology and Infection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1684118224002111 |
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| author | Hua Meng Yuxin Shi Kaming Xue Di Liu Xiongjing Cao Yanyan Wu Yunzhou Fan Fang Gao Ming Zhu Lijuan Xiong |
| author_facet | Hua Meng Yuxin Shi Kaming Xue Di Liu Xiongjing Cao Yanyan Wu Yunzhou Fan Fang Gao Ming Zhu Lijuan Xiong |
| author_sort | Hua Meng |
| collection | DOAJ |
| description | Background: Ventilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked. Methods: This study aimed to establish a weighted risk scoring system to examine VAP risk among a two-stage VAP case-control study, and to evaluate the diagnostic performance of risk factor score (RFS) for VAP. We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. Finally, the diagnostic performance of the RFS was testified and further validated in another 177 pairs of VAP case-control study. Results: LASSO, RF and XGBoost consistently revealed significant associations of length of stay before MV, MV time, surgery, tracheotomy, multiple drug resistant organism infection, C-reactive protein, PaO2, and APACHE II score with VAP. RFS was significantly linearly associated with VAP risk [odds ratio and 95 % confidence interval = 2.699 (2.347, 3.135)], and showed good discriminations for VAP both in discovery stage [area under the curve (AUC) = 0.857] and validation stage (AUC = 0.879). Conclusions: Results of this study revealed co-occurrence of multiple predictors for VAP risk. The risk factor scoring system proposed is a potentially useful predictive tool for clinical targets for VAP prevention. |
| format | Article |
| id | doaj-art-b0995f0adffe4ee9bfb85c1136e54677 |
| institution | Kabale University |
| issn | 1684-1182 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Microbiology, Immunology and Infection |
| spelling | doaj-art-b0995f0adffe4ee9bfb85c1136e546772025-02-06T05:11:21ZengElsevierJournal of Microbiology, Immunology and Infection1684-11822025-02-0158194102Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control studyHua Meng0Yuxin Shi1Kaming Xue2Di Liu3Xiongjing Cao4Yanyan Wu5Yunzhou Fan6Fang Gao7Ming Zhu8Lijuan Xiong9Department of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Integrated Traditional Chinese and Western Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaInterventional Diagnostic and Therapeutic Center, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Corresponding author. Department of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 JieFang Avenue, Wuhan, 430022, China.Background: Ventilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked. Methods: This study aimed to establish a weighted risk scoring system to examine VAP risk among a two-stage VAP case-control study, and to evaluate the diagnostic performance of risk factor score (RFS) for VAP. We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. Finally, the diagnostic performance of the RFS was testified and further validated in another 177 pairs of VAP case-control study. Results: LASSO, RF and XGBoost consistently revealed significant associations of length of stay before MV, MV time, surgery, tracheotomy, multiple drug resistant organism infection, C-reactive protein, PaO2, and APACHE II score with VAP. RFS was significantly linearly associated with VAP risk [odds ratio and 95 % confidence interval = 2.699 (2.347, 3.135)], and showed good discriminations for VAP both in discovery stage [area under the curve (AUC) = 0.857] and validation stage (AUC = 0.879). Conclusions: Results of this study revealed co-occurrence of multiple predictors for VAP risk. The risk factor scoring system proposed is a potentially useful predictive tool for clinical targets for VAP prevention.http://www.sciencedirect.com/science/article/pii/S1684118224002111Ventilator-associated pneumoniaPrediction modelRisk factor scoring systemMachine-learningCase-control study |
| spellingShingle | Hua Meng Yuxin Shi Kaming Xue Di Liu Xiongjing Cao Yanyan Wu Yunzhou Fan Fang Gao Ming Zhu Lijuan Xiong Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study Journal of Microbiology, Immunology and Infection Ventilator-associated pneumonia Prediction model Risk factor scoring system Machine-learning Case-control study |
| title | Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study |
| title_full | Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study |
| title_fullStr | Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study |
| title_full_unstemmed | Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study |
| title_short | Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study |
| title_sort | prediction model risk factor score and ventilator associated pneumonia a two stage case control study |
| topic | Ventilator-associated pneumonia Prediction model Risk factor scoring system Machine-learning Case-control study |
| url | http://www.sciencedirect.com/science/article/pii/S1684118224002111 |
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