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Updated: Jun 9, 2025
Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
[Research progress of cardiovascular disease risk prediction models among patients with chronic kidney disease]
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Insights
Cardiovascular disease (CVD) risk prediction models for chronic kidney disease (CKD) patients often overestimate risk and show significant variability. More independent validation is needed, especially in developing countries, to improve patient management.
Area of Science:
- Nephrology
- Cardiology
- Epidemiology
Context:
- Patients with chronic kidney disease (CKD) face elevated risks of cardiovascular disease (CVD).
- Accurate CVD risk stratification is crucial for effective management of CKD populations.
- Existing CVD risk prediction models require careful evaluation within the CKD context.
Purpose:
- To review and analyze the characteristics and performance of CVD risk prediction models used in CKD populations.
- To identify variability in outcome events, predictive variables, modeling techniques, and predictive performance.
- To assess the availability and quality of model validation, particularly in developing nations.
Summary:
- A review of CVD risk prediction models in CKD populations revealed substantial heterogeneity in study designs, outcome measures, and predictors.
- Models frequently demonstrated an overestimation of CVD risk in CKD patients.
- There is a notable scarcity of independently validated models and external validation studies, especially concerning model calibration in developing countries.
Impact:
- Highlights the need for standardized reporting and rigorous validation of CVD risk prediction models for CKD.
- Informs the development of more accurate and reliable tools for CVD risk assessment in CKD patients.
- Aims to improve clinical decision-making and patient outcomes by enhancing the utility of risk prediction models.
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.
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