The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models
Abstract Objective Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-023-00995-x |
| _version_ | 1797946007210688512 |
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| author | Zixiang Ye Shuoyan An Yanxiang Gao Enmin Xie Xuecheng Zhao Ziyu Guo Yike Li Nan Shen Jingyi Ren Jingang Zheng |
| author_facet | Zixiang Ye Shuoyan An Yanxiang Gao Enmin Xie Xuecheng Zhao Ziyu Guo Yike Li Nan Shen Jingyi Ren Jingang Zheng |
| author_sort | Zixiang Ye |
| collection | DOAJ |
| description | Abstract Objective Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods. Methods Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) algorithm was applied to explain the model visually. Moreover, data from the Telehealth Intensive Care Unit Collaborative Research Database (eICU-CRD) were acquired as an external validation set. Results 3590 and 1657 CKD patients with CAD were acquired from MIMIC-IV and eICU-CRD databases, respectively. A total of 78 variables were selected for the machine learning model development process. Comparatively, GBDT had the highest predictive performance according to the results of AUC (0.946) and AP (0.778). The SHAP method reveals the top 20 factors based on the importance ranking. In addition, GBDT had good predictive value and a certain degree of clinical value in the external validation according to the AUC (0.865), AP (0.672), decision curve analysis, and calibration curve. Conclusion Machine learning algorithms, especially GBDT, can be reliable tools for accurately predicting the in-hospital mortality risk for CKD patients with CAD in the ICU. This contributed to providing optimal resource allocation and reducing in-hospital mortality by tailoring precise management and implementation of early interventions. |
| first_indexed | 2024-04-10T21:04:04Z |
| format | Article |
| id | doaj.art-3d686d1f01db4a42a14ca34d43536c30 |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-10T21:04:04Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-3d686d1f01db4a42a14ca34d43536c302023-01-22T12:08:02ZengBMCEuropean Journal of Medical Research2047-783X2023-01-0128111310.1186/s40001-023-00995-xThe prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning modelsZixiang Ye0Shuoyan An1Yanxiang Gao2Enmin Xie3Xuecheng Zhao4Ziyu Guo5Yike Li6Nan Shen7Jingyi Ren8Jingang Zheng9Department of Cardiology, Peking University China-Japan Friendship School of Clinical MedicineDepartment of Cardiology, China-Japan Friendship HospitalDepartment of Cardiology, China-Japan Friendship HospitalGraduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Cardiology, China-Japan Friendship HospitalDepartment of Cardiology, Peking University China-Japan Friendship School of Clinical MedicineGraduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of Cardiology, Peking University China-Japan Friendship School of Clinical MedicineDepartment of Cardiology, China-Japan Friendship HospitalDepartment of Cardiology, Peking University China-Japan Friendship School of Clinical MedicineAbstract Objective Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods. Methods Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) algorithm was applied to explain the model visually. Moreover, data from the Telehealth Intensive Care Unit Collaborative Research Database (eICU-CRD) were acquired as an external validation set. Results 3590 and 1657 CKD patients with CAD were acquired from MIMIC-IV and eICU-CRD databases, respectively. A total of 78 variables were selected for the machine learning model development process. Comparatively, GBDT had the highest predictive performance according to the results of AUC (0.946) and AP (0.778). The SHAP method reveals the top 20 factors based on the importance ranking. In addition, GBDT had good predictive value and a certain degree of clinical value in the external validation according to the AUC (0.865), AP (0.672), decision curve analysis, and calibration curve. Conclusion Machine learning algorithms, especially GBDT, can be reliable tools for accurately predicting the in-hospital mortality risk for CKD patients with CAD in the ICU. This contributed to providing optimal resource allocation and reducing in-hospital mortality by tailoring precise management and implementation of early interventions.https://doi.org/10.1186/s40001-023-00995-xMIMIC-IV databaseIn-hospital mortalityChronic kidney diseaseCoronary artery diseaseMachine learning |
| spellingShingle | Zixiang Ye Shuoyan An Yanxiang Gao Enmin Xie Xuecheng Zhao Ziyu Guo Yike Li Nan Shen Jingyi Ren Jingang Zheng The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models European Journal of Medical Research MIMIC-IV database In-hospital mortality Chronic kidney disease Coronary artery disease Machine learning |
| title | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| title_full | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| title_fullStr | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| title_full_unstemmed | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| title_short | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| title_sort | prediction of in hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| topic | MIMIC-IV database In-hospital mortality Chronic kidney disease Coronary artery disease Machine learning |
| url | https://doi.org/10.1186/s40001-023-00995-x |
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