The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models

European Journal of Medical Research

Research

Shuoyan An¹  Yanxiang Gao¹  Xuecheng Zhao¹  Jingyi Ren¹  Zixiang Ye²  Nan Shen²  Ziyu Guo²  Jingang Zheng³  Yike Li⁴  Enmin Xie⁴ 

[1] Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, 100029, Beijing, China;Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, 100029, Beijing, China;Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, 100029, Beijing, China;Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, Chaoyang District, 100029, Beijing, China;Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, 100029, Beijing, China;

关键词: MIMIC-IV database;    In-hospital mortality;    Chronic kidney disease;    Coronary artery disease;    Machine learning;   

DOI  :  10.1186/s40001-023-00995-x

 received in 2022-11-27, accepted in 2023-01-04,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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