Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Ying Shan; Yucong Zhang; Yanping Zhao; Yueqi Lu; Bangwei Chen; Liuqiao Yang; Cong Tan; Yong Bai; Yu Sang; Juehan Liu; Min Jian; Lei Ruan; Cuntai Zhang; Tao Li

doi:10.3389/fcvm.2022.967097

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Front Cardiovasc Med. 2022 Nov 18:9:967097. doi: 10.3389/fcvm.2022.967097. eCollection 2022.

Authors

Ying Shan^{1

2}, Yucong Zhang³, Yanping Zhao¹, Yueqi Lu¹, Bangwei Chen^{1

4}, Liuqiao Yang^{1

5}, Cong Tan¹, Yong Bai¹, Yu Sang³, Juehan Liu¹, Min Jian¹, Lei Ruan³, Cuntai Zhang³, Tao Li¹

Affiliations

¹ BGI-Shenzhen, Shenzhen, China.
² Peking University Shenzhen Hospital, Clinical Research Academy, Shenzhen, China.
³ Department of Geriatrics, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
⁴ School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
⁵ College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.

Abstract

Background: Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention.

Methods: We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008-2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions.

Results: CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738-0.799), and D statistic was 4.738 (95% CI: 3.270-6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction.

Conclusions: We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator-calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.

Keywords: Chinese males; cardiovascular diseases; chronic disease prevention; prediction model; retrospective cohort study.