Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency
Background: Coronary Heart Disease (CHD) with renal insufficiency is a significant global health issue. This study aimed to develop and validate a predictive model for in-hospital mortality to enable early risk identification in these patients. Methods: We analyzed data from 11,830 CHD patients with...
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Elsevier
2025-09-01
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| Series: | International Journal of Cardiology. Cardiovascular Risk and Prevention |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772487525001011 |
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| author | Yahui Li Hongsen Cai Wei Zheng Meijie Wang Man Huang Luyun Wang Daowen Wang Chunxia Zhao Wenguang Hou Hu Ding Yan Wang Hongling Zhu |
| author_facet | Yahui Li Hongsen Cai Wei Zheng Meijie Wang Man Huang Luyun Wang Daowen Wang Chunxia Zhao Wenguang Hou Hu Ding Yan Wang Hongling Zhu |
| author_sort | Yahui Li |
| collection | DOAJ |
| description | Background: Coronary Heart Disease (CHD) with renal insufficiency is a significant global health issue. This study aimed to develop and validate a predictive model for in-hospital mortality to enable early risk identification in these patients. Methods: We analyzed data from 11,830 CHD patients with renal insufficiency treated at Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (1994–2023). Among 113 clinical variables, five key features—age, high-sensitivity C-reactive protein (hs-CRP), estimated glomerular filtration rate (eGFR), creatine kinase (CK), and blood urea—were selected using Recursive Feature Elimination. Six machine learning models (Random Forest, XGBoost, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine) were developed and assessed for discrimination, calibration, and clinical utility. Temporal validation was performed using data from May 16, 2023 to October 31, 2024. SHapley Additive exPlanations (SHAP) were used for model interpretation. Results: Of the 11,830 patients, 694 (5.9 %) died during hospitalization. Among the six models, XGBoost showed the best overall performance in the test set, achieving the highest AUC (0.926), lowest Brier score (0.034), highest accuracy (0.957), and balanced sensitivity (0.381) and F1 score (0.512). Decision curve analysis confirmed its superior clinical utility. In a temporally independent validation cohort of 5983 patients, XGBoost maintained strong predictive performance (AUC = 0.901), demonstrating excellent robustness and generalizability. Conclusions: The XGBoost-based model accurately predicts in-hospital mortality in CHD patients with renal insufficiency, supporting early risk stratification and clinical decision-making. |
| format | Article |
| id | doaj-art-eef3e49b65064110b8e050f95c81a836 |
| institution | Kabale University |
| issn | 2772-4875 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Cardiology. Cardiovascular Risk and Prevention |
| spelling | doaj-art-eef3e49b65064110b8e050f95c81a8362025-08-24T05:15:23ZengElsevierInternational Journal of Cardiology. Cardiovascular Risk and Prevention2772-48752025-09-012620046310.1016/j.ijcrp.2025.200463Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiencyYahui Li0Hongsen Cai1Wei Zheng2Meijie Wang3Man Huang4Luyun Wang5Daowen Wang6Chunxia Zhao7Wenguang Hou8Hu Ding9Yan Wang10Hongling Zhu11Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, China; Corresponding authors.College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430030, China; Corresponding authors.Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, China; Corresponding authors.Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, China; Corresponding author.Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Ave., Wuhan, 430030, China; Corresponding author.Background: Coronary Heart Disease (CHD) with renal insufficiency is a significant global health issue. This study aimed to develop and validate a predictive model for in-hospital mortality to enable early risk identification in these patients. Methods: We analyzed data from 11,830 CHD patients with renal insufficiency treated at Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (1994–2023). Among 113 clinical variables, five key features—age, high-sensitivity C-reactive protein (hs-CRP), estimated glomerular filtration rate (eGFR), creatine kinase (CK), and blood urea—were selected using Recursive Feature Elimination. Six machine learning models (Random Forest, XGBoost, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine) were developed and assessed for discrimination, calibration, and clinical utility. Temporal validation was performed using data from May 16, 2023 to October 31, 2024. SHapley Additive exPlanations (SHAP) were used for model interpretation. Results: Of the 11,830 patients, 694 (5.9 %) died during hospitalization. Among the six models, XGBoost showed the best overall performance in the test set, achieving the highest AUC (0.926), lowest Brier score (0.034), highest accuracy (0.957), and balanced sensitivity (0.381) and F1 score (0.512). Decision curve analysis confirmed its superior clinical utility. In a temporally independent validation cohort of 5983 patients, XGBoost maintained strong predictive performance (AUC = 0.901), demonstrating excellent robustness and generalizability. Conclusions: The XGBoost-based model accurately predicts in-hospital mortality in CHD patients with renal insufficiency, supporting early risk stratification and clinical decision-making.http://www.sciencedirect.com/science/article/pii/S2772487525001011Coronary heart diseaseRenal insufficiencyMachine learningDeathShapley additive explanation |
| spellingShingle | Yahui Li Hongsen Cai Wei Zheng Meijie Wang Man Huang Luyun Wang Daowen Wang Chunxia Zhao Wenguang Hou Hu Ding Yan Wang Hongling Zhu Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency International Journal of Cardiology. Cardiovascular Risk and Prevention Coronary heart disease Renal insufficiency Machine learning Death Shapley additive explanation |
| title | Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency |
| title_full | Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency |
| title_fullStr | Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency |
| title_full_unstemmed | Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency |
| title_short | Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency |
| title_sort | development and validation of a predictive model for in hospital mortality in patients with coronary heart disease and renal insufficiency |
| topic | Coronary heart disease Renal insufficiency Machine learning Death Shapley additive explanation |
| url | http://www.sciencedirect.com/science/article/pii/S2772487525001011 |
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