title: Prognostic models in coronary artery disease: Cox and network approaches.
abstract: Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg(-1) over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic factors involved in prognostic stratification whose complexity suggests an exploration beyond the analysis provided by the still fundamental Cox approach.
authors.affiliation: Institute of Clinical Physiology , National Research Council , Pisa, Italy ; Laboratory of Integrative Systems Medicine (LISM) , Institute of Clinical Physiology, National Research Council , Pisa, Italy.
authors.forename: Antonio
authors.initials: A
authors.lastname: Mora
authors.affiliation: Institute of Clinical Physiology , National Research Council , Pisa, Italy.
authors.forename: Rosa
authors.initials: R
authors.lastname: Sicari
authors.affiliation: Cardiology Division , Campo di Marte Hospital , Lucca, Italy.
authors.forename: Lauro
authors.initials: L
authors.lastname: Cortigiani
authors.affiliation: Institute of Clinical Physiology , National Research Council , Pisa, Italy.
authors.forename: Clara
authors.initials: C
authors.lastname: Carpeggiani
authors.affiliation: Institute of Clinical Physiology , National Research Council , Pisa, Italy.
authors.forename: Eugenio
authors.initials: E
authors.lastname: Picano
authors.affiliation: Institute of Clinical Physiology , National Research Council , Pisa, Italy ; Laboratory of Integrative Systems Medicine (LISM) , Institute of Clinical Physiology, National Research Council , Pisa, Italy ; Center for Computational Science , University of Miami , Coral Gables, FL 33146, USA.
authors.forename: Enrico
authors.initials: E
authors.lastname: Capobianco
keywords: coronary artery disease; cox models; mutual information network