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Individual risk prediction model for incident cardiovascular disease: a Bayesian clinical reasoning approach
Yi-Ming Liu1, Sam Li-Sheng Chen, Amy Ming-Fang Yen
1Division of Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Insights
This study presents a Bayesian model for predicting cardiovascular disease (CVD) risk. The model sequentially incorporates demographic, metabolic syndrome, and conventional risk factors for enhanced individual risk assessment.
Area of Science:
- Cardiology
- Biostatistics
- Public Health
Background:
- Cardiovascular disease (CVD) poses a significant global health challenge.
- Accurate individual risk prediction is crucial for effective CVD prevention strategies.
Purpose of the Study:
- To develop and validate a Bayesian clinical reasoning model for predicting individual cardiovascular disease (CVD) risk.
- To sequentially incorporate demographic, metabolic syndrome, and conventional risk factors into the CVD risk prediction model.
Main Methods:
- Three Bayesian models were constructed: basic (demographic), metabolic score, and enhanced (conventional risk factors).
- Clinical weights (regression coefficients) were treated as normal distributions to account for uncertainty.
- A community-based cohort of 64,489 participants, free of CVD at baseline, was used for model illustration.
Main Results:
- The models can predict CVD risk with various combinations of risk factors.
- For a 47-year-old man, the five-year CVD risk increased from 11.2% (basic model) to 15.8% (metabolic score) and 30.9% (enhanced model).
- Metabolic syndrome components and conventional risk factors like smoking and family history significantly increased CVD risk predictions.
Conclusions:
- A sequential Bayesian clinical reasoning approach effectively predicts individual CVD risk.
- The model integrates routine clinical information for practical risk assessment.
- This approach enhances the precision of cardiovascular disease risk stratification.
Background:
A Bayesian clinical reasoning model was developed to predict an individual risk for cardiovascular disease (CVD) for desk-top reference.
Methods:
Three Bayesian models were constructed to estimate the CVD risk by sequentially incorporating demographic features (basic), six metabolic syndrome components (metabolic score) and conventional risk factors (enhanced model). By considering clinical weights (regression coefficients) of each model as normal distribution, individual risk can be predicted making allowance for uncertainty of clinical weights. A community-based cohort that enrolled 64,489 participants free of CVD at baseline and followed up over five years to ascertain newly diagnosed CVD cases during the period through 2000 to 2004 was used for the illustration of the three proposed models (full empirical data are available from website http://homepage.ntu.edu.tw/~chenlin/CVD_prediction_data.rar).
Results:
The proposed models can be applied to predicting the CVD risk with any combination of risk factors. For a 47-year-old man, the five-year risk for CVD with the basic model was 11.2% (95% CI: 7.8%-15.6%). His metabolic syndrome score, leading to 1.488 of likelihood ratio, enhanced the risk for CVD up to 15.8% (95% CI: 11.0%-21.5%) and put him in highest deciles. As with the habit of smoking over 2 packs per-day and family history of CVD, yielding the likelihood ratios of 1.62 and 1.47, respectively, the risk was further raised to 30.9% (95% CI: 20.7%-39.8%).
Conclusions:
We demonstrate how to make individual risk prediction for CVD by incorporating routine information with a sequential Bayesian clinical reasoning approach.
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