The summaries are free for public
use. The Chronic Liver Disease
Foundation will continue to add and
archive summaries of articles deemed
relevant to CLDF by the Board of
Trustees and its Advisors.
Abstract Details
Bayesian Network Modelling Study to Identify Factors Influencing the Risk of Cardiovascular Disease in Canadian Adults With Hepatitis C Virus Infection
MJ Open. 2020 May 5;10(5):e035867. doi: 10.1136/bmjopen-2019-035867.
Alaa Badawi12, Giancarlo Di Giuseppe34, Alind Gupta5, Abbey Poirier6, Paul Arora3
Author information
1Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, Ontario, Canada alaa.badawi@canada.ca.
2Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
3Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
4Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada.
5Lighthouse Outcomes, Toronto, Ontario, Canada.
6Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Alberta, Canada.
Free PMC article
Abstract
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; DKL ) showed diabetes as a major effect modifier on the joint probability distribution of HCV infection and CVD risk (DKL =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.