Integrating stress-related ventricular functional and angiographic data in preventive cardiology: a unified approach implementing a Bayesian network

Background Identification of key factors associated with the risk of adverse cardiovascular events and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology. Methods In the present paper, we examined clinical predictors of adverse cardiovascular events among 228 individuals with symptoms suggestive of coronary artery disease (CAD) undergoing functional (stress echocardiography) and anatomical (coronary angiography) assessment of CAD. Particularly, we evaluate the possibility to integrate simple measures that have known prognostic value and more recently discovered predictors of risk, such as stress-related ventricular function data and angiographic data, in a unique model implementing a Bayesian network (BN). Moreover, we compared the performance of BN and the covariates hierarchy with those obtained from logistic regression model and from a set of alternative tools becoming popular in various clinical settings, including random forest classification tree analysis, artificial neural networks and support vector machine.