A review on cardiovascular disease risk prediction models in the elderly.
10.3760/cma.j.cn112338-20221104-00940
- VernacularTitle:老年人群心血管疾病风险预测模型研究进展
- Author:
Yin Qi DING
1
;
Song Chun YANG
2
;
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.
- Publication Type:Journal Article
- MeSH:
Humans;
Aged;
Cardiovascular Diseases/epidemiology*;
Algorithms;
Machine Learning
- From:
Chinese Journal of Epidemiology
2023;44(6):1013-1020
- CountryChina
- Language:Chinese
-
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.
