###### Abstract  
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; causal graphs; downsampling; logistic regression; Markov blanket; PC; prediction +
Footnote †: journal: Medical  
graphic data, clinical, anatomic, procedure parameters, etc.). The motivation in this study was to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models are used for the first time in this context to the best of our knowledge. Current approaches are mainly based on standard regression analysis and standard correlation assessment, to detect the most appropriate predictor variables for the response variable of interest. Causal models, on the other hand, aim to discover the cause-effect relationships between variables. The focus in this paper was to find and use the local causal structure around the response/target variable (i.e., operation result) to predict its outcome, particularly the so-called Markov blanket. By definition, the Markov blanket of a variable T is the minimal variable subset conditioned on which all other variables are probabilistically independent of T. The Markov blanket of T consists of the variables representing its parents, children, and other parents of its children in the graph (spouses). Using the Markov blanket of the response variable constitutes one of the emerging successful filtering approaches in variable selection [8]. In [9], the authors used causal models, particularly the probabilistic graphical models, to assess whether lung cancer prediction can be improved. The motivation in their approach was that the Markov blanket around a target variable contains, by definition, the most informative variables for the target variable. They found that their modeling approach, which was based on causal graphs, performed better than other competitive models in the literature. In [10], the authors used extensive simulations to assess the usage of Markov blanket as a feature selection tool compared to other methodological schemes. Particularly, they compared the predictive accuracy of eight logistic regression-based models, in which the set of predictors was selected with a different reasoning. Their results demonstrated empirically that the Markov blanket-based logistic regression model performed equal to or better than all its competitors. The authors argued that using causal models to identify the Markov blanket of a target variable might be a useful, efficient strategy to select predictors in clinical risk prediction models.