| 奚梓玮,莫竞娴,刘秋萍,唐迅,高培.慢性肾脏病人群心血管病风险预测模型研究进展[J].Chinese journal of Epidemiology,2024,45(10):1448-1454 |
| 慢性肾脏病人群心血管病风险预测模型研究进展 |
| Research progress of cardiovascular disease risk prediction models among patients with chronic kidney disease |
| Received:May 22, 2024 |
| DOI：10.3760/cma.j.cn112338-20240522-00296 |
| KeyWord: 风险预测模型 心血管病 慢性肾脏病 |
| English Key Word: Risk prediction model Cardiovascular disease Chronic kidney disease |
| FundProject:国家重点研发计划（2020YFC2003503）；国家自然科学基金（82373662） |
| Author Name | Affiliation | E-mail | | Xi Ziwei | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Mo Jingxian | Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China | | | Liu Qiuping | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | liuqiuping@bjmu.edu.cn | | Tang Xun | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China | | | Gao Pei | Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Center for Real-world Evidence Evaluation, Peking University Clinical Research Institute, Beijing 100191, China | peigao@bjmu.edu.cn |
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| Abstract: |
| 慢性肾脏病（CKD）人群有较高的心血管病发病和死亡风险，准确的风险预测是开展CKD人群心血管病风险分层和精准管理的基础。本文综述了国内外针对CKD人群构建的心血管病风险预测模型的结局事件、预测变量、建模方法和模型的预测能力，发现不同模型在结局事件定义、预测变量的数量和样本量方面相差较大，且倾向于高估CKD人群的心血管病风险；当前针对发展中国家的CKD人群独立验证或构建的心血管病风险预测模型较少，尤其缺乏针对模型校准度的独立外部验证研究。后续研究需结合偏倚风险和适用性评估工具以及风险预测模型建模报告规范开展相关研究。 |
| English 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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