PMID- 36653875
OWN - NLM
STAT- MEDLINE
DCOM- 20230120
LR  - 20230122
IS  - 2047-783X (Electronic)
IS  - 0949-2321 (Print)
IS  - 0949-2321 (Linking)
VI  - 28
IP  - 1
DP  - 2023 Jan 18
TI  - The prediction of in-hospital mortality in chronic kidney disease patients with 
      coronary artery disease using machine learning models.
PG  - 33
LID - 10.1186/s40001-023-00995-x [doi]
LID - 33
AB  - 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.
CI  - (c) 2023. The Author(s).
FAU - Ye, Zixiang
AU  - Ye Z
AD  - Department of Cardiology, Peking University China-Japan Friendship School of 
      Clinical Medicine, Beijing, 100029, China.
FAU - An, Shuoyan
AU  - An S
AD  - Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, 
      Chaoyang District, Beijing, 100029, China.
FAU - Gao, Yanxiang
AU  - Gao Y
AD  - Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, 
      Chaoyang District, Beijing, 100029, China.
FAU - Xie, Enmin
AU  - Xie E
AD  - Graduate School of Peking Union Medical College, Chinese Academy of Medical 
      Sciences and Peking Union Medical College, Beijing, 100029, China.
FAU - Zhao, Xuecheng
AU  - Zhao X
AD  - Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, 
      Chaoyang District, Beijing, 100029, China.
FAU - Guo, Ziyu
AU  - Guo Z
AD  - Department of Cardiology, Peking University China-Japan Friendship School of 
      Clinical Medicine, Beijing, 100029, China.
FAU - Li, Yike
AU  - Li Y
AD  - Graduate School of Peking Union Medical College, Chinese Academy of Medical 
      Sciences and Peking Union Medical College, Beijing, 100029, China.
FAU - Shen, Nan
AU  - Shen N
AD  - Department of Cardiology, Peking University China-Japan Friendship School of 
      Clinical Medicine, Beijing, 100029, China.
FAU - Ren, Jingyi
AU  - Ren J
AD  - Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, 
      Chaoyang District, Beijing, 100029, China. renjingyi1213@163.com.
FAU - Zheng, Jingang
AU  - Zheng J
AD  - Department of Cardiology, Peking University China-Japan Friendship School of 
      Clinical Medicine, Beijing, 100029, China. mdjingangzheng@163.com.
AD  - Department of Cardiology, China-Japan Friendship Hospital, 2 Yinghua Dongjie, 
      Chaoyang District, Beijing, 100029, China. mdjingangzheng@163.com.
LA  - eng
PT  - Journal Article
DEP - 20230118
PL  - England
TA  - Eur J Med Res
JT  - European journal of medical research
JID - 9517857
SB  - IM
MH  - Humans
MH  - *Coronary Artery Disease
MH  - Hospital Mortality
MH  - Algorithms
MH  - Machine Learning
MH  - *Renal Insufficiency, Chronic
PMC - PMC9847092
OTO - NOTNLM
OT  - Chronic kidney disease
OT  - Coronary artery disease
OT  - In-hospital mortality
OT  - MIMIC-IV database
OT  - Machine learning
COIS- Not applicable.
EDAT- 2023/01/19 06:00
MHDA- 2023/01/21 06:00
PMCR- 2023/01/18
CRDT- 2023/01/18 23:40
PHST- 2022/11/27 00:00 [received]
PHST- 2023/01/04 00:00 [accepted]
PHST- 2023/01/18 23:40 [entrez]
PHST- 2023/01/19 06:00 [pubmed]
PHST- 2023/01/21 06:00 [medline]
PHST- 2023/01/18 00:00 [pmc-release]
AID - 10.1186/s40001-023-00995-x [pii]
AID - 995 [pii]
AID - 10.1186/s40001-023-00995-x [doi]
PST - epublish
SO  - Eur J Med Res. 2023 Jan 18;28(1):33. doi: 10.1186/s40001-023-00995-x.