A Bayesian Belief Network model for the estimation of risk of cardiovascular events in subjects with type 1 diabetes

Abstract

Objectives:

Cardiovascular diseases (CVDs) represent a major risk for people with type 1 diabetes (T1D). Our aim here is to develop a new methodology that overcomes some of the problems and limitations of existing risk calculators. First, they are rarely tailored to people with T1D and, in general, they do not deal with missing values for any risk factor. Moreover, they do not take into account information on risk factors dependencies, which is often available from medical experts.

Method:

This study introduces a Bayesian Belief Network (BBN) model to quantify CVD risk in individuals with T1D. The developed methodology is applied to a large T1D dataset and its performances are assessed. A simulation study is also carried out to quantify the parameter estimation properties.

Results:

The performances of individual risk estimation, as measured by the area under the ROC curve and by the C-index, are about 0.75 for both real and simulated data with comparable sample sizes.

Conclusions:

We observe a good predictive ability of the proposed methodology with accurate parameter estimation. The BBN approach takes into account causal relationships between variables, providing a comprehensive description of the system. This makes it possible to derive useful tools for optimising intervention.