Individual risk prediction model for incident cardiovascular disease

A Bayesian clinical reasoning approach

Yi Ming Liu, Sam Li Sheng Chen, Amy Ming Fang Yen, Hsiu Hsi Chen

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2008-2012
Number of pages5
JournalInternational Journal of Cardiology
Volume167
Issue number5
DOIs
Publication statusPublished - Sep 1 2013

Fingerprint

Cardiovascular Diseases
Weights and Measures
Bayes Theorem
Normal Distribution
Uncertainty
Habits
Smoking
Odds Ratio
Demography

Keywords

  • Bayes' theorem
  • Bayesian
  • Cardiovascular disease
  • Likelihood ratio
  • Metabolic syndrome
  • Prediction model

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Individual risk prediction model for incident cardiovascular disease : A Bayesian clinical reasoning approach. / Liu, Yi Ming; Chen, Sam Li Sheng; Yen, Amy Ming Fang; Chen, Hsiu Hsi.

In: International Journal of Cardiology, Vol. 167, No. 5, 01.09.2013, p. 2008-2012.

Research output: Contribution to journalArticle

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abstract = "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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