TY - JOUR
T1 - Individual risk prediction model for incident cardiovascular disease
T2 - A Bayesian clinical reasoning approach
AU - Liu, Yi Ming
AU - Chen, Sam Li Sheng
AU - Yen, Amy Ming Fang
AU - Chen, Hsiu Hsi
PY - 2013/9/1
Y1 - 2013/9/1
N2 - 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.
AB - 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.
KW - Bayes' theorem
KW - Bayesian
KW - Cardiovascular disease
KW - Likelihood ratio
KW - Metabolic syndrome
KW - Prediction model
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U2 - 10.1016/j.ijcard.2012.05.016
DO - 10.1016/j.ijcard.2012.05.016
M3 - Article
C2 - 22658349
AN - SCOPUS:84883264382
SN - 0167-5273
VL - 167
SP - 2008
EP - 2012
JO - International Journal of Cardiology
JF - International Journal of Cardiology
IS - 5
ER -