| Title | The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models |
| Authors | Ye, Zixiang An, Shuoyan Gao, Yanxiang Xie, Enmin Zhao, Xuecheng Guo, Ziyu Li, Yike Shen, Nan Ren, Jingyi Zheng, Jingang |
| Affiliation | Peking Univ China, Japan Friendship Sch Clin Med, Dept Cardiol, Beijing 100029, Peoples R China China Japan Friendship Hosp, Dept Cardiol, 2 Yinghua Dongjie, Beijing 100029, Peoples R China Chinese Acad Med Sci & Peking Union Med Coll, Grad Sch Peking Union Med Coll, Beijing 100029, Peoples R China |
| Keywords | STAGE RENAL-DISEASE HEMODIALYSIS-PATIENTS RISK STRATIFICATION PARATHYROID-HORMONE CALCIUM INTERVENTION ASSOCIATION PHOSPHATE PRODUCT |
| Issue Date | 18-Jan-2023 |
| Publisher | EUROPEAN JOURNAL OF MEDICAL RESEARCH |
| Abstract | ObjectiveChronic 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.MethodsData 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.Results3590 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.ConclusionMachine 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. |
| URI | http://hdl.handle.net/20.500.11897/668379 |
| ISSN | 0949-2321 |
| DOI | 10.1186/s40001-023-00995-x |
| Indexed | SCI(E) |
| Appears in Collections: | 待认领 |
