Progress in research of risk prediction model for chronic kidney disease.
10.3760/cma.j.cn112338-20220908-00771
- Author:
Zhi Qng ZENG
1
;
Song Chun YANG
2
;
Can Qing YU
3
;
Lu Xia ZHANG
4
;
Jun LYU
3
;
Li Ming LI
3
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
2. Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China.
3. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
4. National Institute of Health Data Science of Peking University, Beijing 100191, China Department of Nephrology, Peking University First Hospital/Institute of Nephrology, Peking University, Beijing 100034, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Renal Insufficiency, Chronic;
Risk Factors;
Biomarkers
- From:
Chinese Journal of Epidemiology
2023;44(3):498-503
- CountryChina
- Language:Chinese
-
Abstract:
Chronic kidney disease (CKD) is an important global public health problem that greatly threatens population health. Application of risk prediction model is a crucial way for the primary prevention of CKD, which can stratify the risk for developing CKD and identify high-risk individuals for more intensive interventions. By now, more than twenty risk prediction models for CKD have been developed worldwide. There are also four domestic risk prediction models developed for Chinese population. However, none of these models have been recommended in clinical guidelines yet. The existing risk prediction models have some limitations in terms of outcome definition, predictors, strategies for handling missing data, and model derivation. In the future, the applications of emerging biomarkers and polygenic risk scores as well as advances in machine learning methods will provide more possibilities for the further improvement of the model.
