| 曾芷青,杨淞淳,余灿清,张路霞,吕筠,李立明.慢性肾脏病发病风险预测模型研究进展[J].Chinese journal of Epidemiology,2023,44(3):498-503 |
| 慢性肾脏病发病风险预测模型研究进展 |
| Progress in research of risk prediction model for chronic kidney disease |
| Received:September 08, 2022 |
| DOI：10.3760/cma.j.cn112338-20220908-00771 |
| KeyWord: 慢性肾脏病 风险预测模型 一级预防 |
| English Key Word: Chronic kidney disease Risk prediction model Primary prevention |
| FundProject:国家自然科学基金（82192904，82192901，82192900）；中央高校基本科研业务费医-X种子基金（BMU2022MX025） |
| Author Name | Affiliation | E-mail | | Zeng Zhiqing | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Yang Songchun | Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China | | | Yu Canqing | 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 | | | Zhang Luxia | 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 | | | Lyu Jun | 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 | lvjun@bjmu.edu.cn | | Li Liming | 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 | |
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| Abstract: |
| 慢性肾脏病（CKD）是全球重要的公共卫生问题，严重危害人群健康。利用预测模型对人群未来一段时间的CKD发病风险进行分层，针对高危人群采取干预措施是实现CKD一级预防的重要途径。世界范围内已经开发出了二十多个CKD发病风险预测模型，我国学者也开发出了4个适用于中国人群的预测模型，但目前的临床指南中尚未推荐使用任何专门的CKD风险预测模型。现有模型在结局定义、预测因子、缺失数据处理和建模方法选择方面仍有局限。在未来，新兴生物标志物和多基因风险评分的应用以及机器学习方法的发展将为继续改进模型提供更多可能。 |
| English 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. |
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