| 丁银圻,杨淞淳,吕筠,李立明.老年人群心血管疾病风险预测模型研究进展[J].Chinese journal of Epidemiology,2023,44(6):1013-1020 |
| 老年人群心血管疾病风险预测模型研究进展 |
| A review on cardiovascular disease risk prediction models in the elderly |
| Received:November 04, 2022 |
| DOI：10.3760/cma.j.cn112338-20221104-00940 |
| KeyWord: 老年人 心血管疾病 风险预测模型 |
| English Key Word: Elderly Cardiovascular disease Risk prediction model |
| FundProject:国家自然科学基金（82192904，82192901，82192900） |
| Author Name | Affiliation | E-mail | | Ding Yinqi | 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 | | | 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: |
| 风险预测模型（模型）对于老年人群心血管疾病（CVD）的一级预防具有重要意义。国内外针对老年人群构建的CVD模型共检索到15篇文献。模型的结局定义差异较大；10个模型报告时缺少方法、结果的重要信息；10个模型存在高偏倚风险；13个模型在内部验证时仅表现出中等区分度；仅有4个模型经过外部验证。老年人群CVD模型在模型算法、预测因子与结局的关联强度方面与一般人群模型存在差异，且老年人群模型的预测能力有所下降。未来仍需补充高质量的外部验证研究证据，并探索增加新的预测因子、采用竞争风险模型算法、机器学习算法、联合模型算法、改变预测时间范围等途径对模型进行优化。 |
| English Abstract: |
| Risk prediction models play an important role in the primary prevention of cardiovascular diseases (CVD) in the elderly population. There are fifteen papers about CVD risk prediction models developed for the elderly domestically and internationally, of which the definitions of disease outcome vary widely. Ten models were reported with insufficient information about study methods or results. Ten models were at high risk of bias. Thirteen models presented moderate discrimination in internal validation, and only four models have undertaken external validation. The CVD risk prediction models for the elderly differed from those for the general population in terms of model algorithm and the effect size of association between predictor and outcome, and the prediction performance of the models for the elderly attenuated. In the future, high-quality external validation researches are necessary to provide more solid evidence. Different ways, including adding new predictors, using competing risk model algorithms, machine learning methods, or joint models, and altering the prediction time horizon, should be explored to optimize the current models. |
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