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Published on: September 16, 2022
Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)
Ying Shan1,2, Yucong Zhang3, Yanping Zhao1
1BGI-Shenzhen, Shenzhen, China.
Insights
Cardiovascular disease (CVD) risk in Chinese males can be better predicted using the new CVDMCM model. This model outperforms existing tools, aiding early intervention for CVD prevention.
Area of Science:
- Epidemiology
- Cardiovascular Health
- Predictive Modeling
Background:
- Cardiovascular diseases (CVD) pose a significant and growing mortality risk in China.
- Identifying individuals at high risk for CVD is crucial for implementing targeted preventive interventions.
- A specific risk prediction model for Chinese males is needed to improve CVD management.
Purpose of the Study:
- To develop and internally validate a novel Cardiovascular Disease Risk Prediction Model for Chinese Males (CVDMCM).
- To compare the performance of CVDMCM against established risk prediction models like the Framingham CVD risk model and Wu's simplified model.
- To provide clinicians with a tool for identifying Chinese males at elevated risk of developing CVD.
Main Methods:
- A retrospective cohort study involving 2,331 Chinese males without baseline CVD was conducted.
- Three predictive models were developed using different predictor sets, including Framingham criteria and LASSO algorithm selection.
- Internal validation utilized bootstrap resampling (1,000 repetitions) to assess model performance based on Harrell's C statistic, D statistic, and calibration.
Main Results:
- The developed CVDMCM demonstrated strong predictive performance with a Harrell's C statistic of 0.769 (95% CI: 0.738-0.799) and a D statistic of 4.738 (95% CI: 3.270-6.864).
- CVDMCM significantly outperformed both the Framingham CVD risk model and Wu's simplified model in predicting 4-year CVD risk.
- Calibration plots and statistical measures confirmed the superior accuracy of CVDMCM.
Conclusions:
- The CVDMCM is a validated tool that offers improved 4-year CVD risk prediction for Chinese males compared to existing models.
- The developed web calculator, calCVDrisk, facilitates easy risk score generation for clinical use.
- The CVDMCM and its associated calculator can aid physicians in identifying high-risk individuals for timely CVD intervention.
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.
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