Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review

作者：Ruixue Cai, Xiaoli Wu, Chuanbao Li, Jianqian Chao · 发表于：Journal of Hypertension · 年份：2020 · DOI：10.1097/hjh.0000000000002442 · 被引用次数：18 · 研究领域：Artificial Intelligence in Healthcare、Artificial Intelligence in Healthcare and Education、Blood Pressure and Hypertension Studies

OBJECTIVE: The aim of this study was to identify, describe, and evaluate the available cardiovascular disease risk prediction models developed or validated in the hypertensive population. METHODS: MEDLINE and the Web of Science were searched from database inception to March 2019, and all reference lists of included articles were reviewed. RESULTS: A total of 4766 references were screened, of which 18 articles were included in the review, presenting 17 prediction models specifically developed for hypertensive populations and 25 external validations. Among the 17 prediction models, most were constructed based on randomized trials in Europe or North America to predict the risk of fatal or nonfatal cardiovascular events. The most common predictors were classic cardiovascular risk factors such as age, diabetes, sex, smoking, and SBP. Of the 17 models, only one model was externally validated. Among the 25 external validations, C-statistics ranged from 0.58 to 0.83, 0.56 to 0.75, and 0.64 to 0.78 for models developed in the hypertensive population, the general population and other specific populations, respectively. Most of the development studies and validation studies had an overall high risk of bias according to PROBAST. CONCLUSION: There are a certain number of cardiovascular risk prediction models in patients with hypertension. The risk of bias assessment showed several shortcomings in the methodological quality and reporting in both the development and validation studies. Most...