Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development.

Coronary chronic total occlusions (CTOs) are very common in patients undergoing coronary angiography. There has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs. The success rate of PCI has been boosted over the last few years by, among else, operator experience and advances in technology, even achieving levels of approximately 90%. This study proposes a prediction model for the classification of the cases in successful and unsuccessful operations and addresses the problem of class imbalance in the response variable (operation result). It is based on the EuroCTO Registry, which is the largest database available worldwide consisting of 29,995 cases for the period 2008-2018. Binary logistic regression analysis and down-sampling were applied within a customized step-algorithm and standard statistical accuracy measures were employed for the assessment of the prediction model, such as sensitivity, specificity and the value of the area under the ROC (AUROC) curve. The analysis revealed new predictive factors, validating at the same time the impact of well-known predictors. A brief comparison has been performed with other models from the literature, which showed that the proposed model performs similarly or better than its contemporary competitors.

An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.