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Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
Guo Zhiting1, Tang Jiaying1, Han Haiying2
1Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), No.88 Jiefang road, Shangcheng District, Hangzhou, 310009, Zhejiang Province, China.
Insights
Cardiovascular disease (CVD) risk models in China are emerging, but their clinical utility requires further validation. This review highlights the need for external validation and local tailoring of these prediction tools for effective primary prevention.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Cardiovascular disease (CVD) is the leading cause of mortality in China.
- Accurate CVD risk identification is crucial for effective primary prevention strategies.
- A systematic review of CVD risk prediction models in the Chinese population is needed.
Purpose of the Study:
- To systematically review CVD risk prediction models derived and/or validated in the Chinese population.
- To assess the current landscape of CVD risk prediction tools for primary prevention in China.
- To identify gaps in research and guide future development.
Main Methods:
- Systematic literature search across multiple databases (PubMed, Embase, CNKI, etc.).
- Inclusion of studies deriving or validating CVD risk prediction models in Chinese populations.
- Risk of bias assessment using PROBAST and meta-analysis where appropriate.
Main Results:
- 22 studies were included, deriving 18 CVD risk prediction models.
- Seven models were based on multicentre Chinese cohorts; one included Chinese individuals in New Zealand.
- Foreign models like Framingham and PCEs were frequently validated; China-PAR showed good calibration.
Conclusions:
- Multiple CVD risk prediction models exist for the Chinese population.
- Incomplete external validation and lack of head-to-head comparisons limit current model utility.
- Future research should prioritize external validation and local adaptation of models.
Background:
There is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or validated in the Chinese population to promote primary CVD prevention.
Methods:
Reports were included if they derived or validated one or more CVD risk prediction models in the Chinese population. PubMed, Embase, CINAHL, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), VIP database, etc., were searched. The risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed in R using the package metamisc.
Results:
From 55,183 records, 22 studies were included. Twelve studies derived 18 CVD risk prediction models, of which seven models were derived based on a multicentre cohort including more than two provinces of mainland China, and one was a model developed based on a New Zealand cohort including Chinese individuals. The number of predictors ranged from 6 to 22. The definitions of predicted outcomes showed considerable heterogeneity. Fourteen articles described 29 validations of 8 models. The Framingham model and pooled cohort equations (PCEs) are the most frequently validated foreign tools. Discrimination was acceptable and similar for men and women among models (0.60-0.83). The calibration estimates changed substantially from one population to another. Prediction for atherosclerotic cardiovascular disease Risk in China (China-PAR) showed good calibration [observed/expected events ratio = 0.99, 95% PI (0.57,1.70)] and female sex [1.10, 95% PI (0.23,5.16)].
Conclusions:
Several models have been developed or validated in the Chinese population. The usefulness of most of the models remains unclear due to incomplete external validation and head-to-head comparison. Future research should focus on externally validating or tailoring these models to local settings.
Trail Registration:
This systematic review was registered at PROSPERO (International Prospective Register of Systematic Reviews, CRD42021277453).
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