Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

OBJECTIVES: The present study evaluates the extent of association between hepatitis C virus (HCV) infection and cardiovascular disease (CVD) risk and identifies factors mediating this relationship using Bayesian network (BN) analysis. DESIGN AND SETTING: A population-based cross-sectional survey in Canada. PARTICIPANTS: Adults from the Canadian Health Measures Survey (n=10 115) aged 30 to 74 years. PRIMARY AND SECONDARY OUTCOME MEASURES: The 10-year risk of CVD was determined using the Framingham Risk Score in HCV-positive and HCV-negative subjects. Using BN analysis, variables were modelled to calculate the probability of CVD risk in HCV infection. RESULTS: When the BN is compiled, and no variable has been instantiated, 73%, 17% and 11% of the subjects had low, moderate and high 10-year CVD risk, respectively. The conditional probability of high CVD risk increased to 13.9%±1.6% (p<2.2×10(-16)) when the HCV variable is instantiated to ‘Present’ state and decreased to 8.6%±0.2% when HCV was instantiated to ‘Absent’ (p<2.2×10(-16)). HCV cases had 1.6-fold higher prevalence of high-CVD risk compared with non-infected individuals (p=0.038). Analysis of the effect modification of the HCV-CVD relationship (using median Kullback-Leibler divergence; D(KL)) showed diabetes as a major effect modifier on the joint probability distribution of HCV infection and CVD risk (D(KL)=0.27, IQR: 0.26 to 0.27), followed by hypertension (0.24, IQR: 0.23 to 0.25), age (0.21, IQR: 0.10 to 0.38) and injection drug use (0.19, IQR: 0.06 to 0.59). CONCLUSIONS: Exploring the relationship between HCV infection and CVD risk using BN modelling analysis revealed that the infection is associated with elevated CVD risk. A number of risk modifiers were identified to play a role in this relationship. Targeting these factors during the course of infection to reduce CVD risk should be studied further.