Ruixue Cai1, Xiaoli Wu2, Chuanbao Li1, Jianqian Chao1. 1. Key Laboratory of Environmental Medicine Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing. 2. Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Abstract
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 models developed in the hypertensive population have not been externally validated. Compared with models developed for the general population and other specific populations, models developed for the hypertensive population do not display a better performance when validated among patients with hypertension. Research is needed to validate and improve the existing cardiovascular disease risk prediction models in hypertensive populations rather than developing completely new models.
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 models developed in the hypertensive population have not been externally validated. Compared with models developed for the general population and other specific populations, models developed for the hypertensive population do not display a better performance when validated among patients with hypertension. Research is needed to validate and improve the existing cardiovascular disease risk prediction models in hypertensive populations rather than developing completely new models.
Authors: Isabela M Bensenor; Alessandra C Goulart; Alexandre C Pereira; André R Brunoni; Airlane Alencar; Raul D Santos; Márcio S Bittencourt; Rosa W Telles; Luciana Andrade Carneiro Machado; Sandhi Maria Barreto; Bianca de Almeida-Pititto; Carolina Porto Silva Janovsky; José Augusto Sgarbi; William R Tebar; Vandrize Meneghini; Fernando Barbosa Junior; Ana Cristina de Medeiros Ribeiro; Sandra Gofinet Pasoto; Rosa Maria R Pereira; Eloísa Bonfá; Aytan M Sipahi; Itamar de S Santos; Paulo A Lotufo Journal: Clinics (Sao Paulo) Date: 2022-04-06 Impact factor: 2.365