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Integrating stress‐related ventricular functional and angiographic data in preventive cardiology: a unified approach implementing a Bayesian network
Author(s) -
Berchialla Paola,
Foltran Francesca,
Bigi Riccardo,
Gregori Dario
Publication year - 2012
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2011.01651.x
Subject(s) - cardiology , medicine , bayesian network , computer science , intensive care medicine , artificial intelligence
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. Results Network graph and results coming from sensitivity analysis, where variables are ranked according to the gain they provided in variance reduction, seem have an easily intuitive lecture: variables that are measure of ventricular disfunction or of the extent of CAD show a greater impact in predicting event. On the other hand, anamnestic data such as diabetes, dyslipidaemia, hypertension, smoke habits, which are related to the outcome throughout a process of intermediate variables, per se have a small role in outcome prediction. BNs are able to explain a relevant part of variance (70%) and have discrimination ability superior or comparable with those to random forest classification tree analysis, artificial neural networks and support vector machine. Discussion Despite the complexity of interactions, model obtained implementing a BN seems to be able to adequately describe the relationships existing among the analysed variables. BN, being able to predict scenarios in which new variables can be incorporated as health process evolves, can measure individual's risks for adverse cardiovascular events, providing a permanent second opinion to the medical practitioner and assisting diagnostic and therapeutic process.