Research progress of cardiovascular disease risk prediction models among patients with chronic kidney disease
10.3760/cma.j.cn112338-20240522-00296
- VernacularTitle:慢性肾脏病人群心血管病风险预测模型研究进展
- Author:
Ziwei XI
1
;
Jingxian MO
;
Qiuping LIU
;
Xun TANG
;
Pei GAO
Author Information
1. 北京大学公共卫生学院流行病与卫生统计学系，北京 100191
- Keywords:
Risk prediction model;
Cardiovascular disease;
Chronic kidney disease
- From:
Chinese Journal of Epidemiology
2024;45(10):1448-1454
- CountryChina
- Language:Chinese
-
Abstract:
Patients with chronic kidney disease (CKD) have a relatively high risk of cardiovascular disease (CVD). Risk stratification guided by CVD risk prediction models is essential for managing CKD populations. We reviewed the outcome events, predictive variables, modeling methods, and predictive performance of CVD risk prediction models in CKD populations. We found a large variability in predictive outcomes, number of predictors, and sample sizes across studies. The models tended to overestimate the CVD risk of CKD populations. There are few independently validated or constructed CVD risk prediction models for CKD populations in developing countries, and in particular, there is a lack of independent external validation studies of model calibration. Future studies should comply with the reporting standards of risk prediction models to better support the application of CVD risk prediction models for CKD populations.
