## 4 Discussion  
CTO is a very common condition for patients that undergo coronary angiography. The increasing acceptance of PCI in CTO in recent years highlights the importance of efficiently detecting the factors that are strongly related with the result of the PCI for prediction model development. Several attempts are available in the literature that assess the impact of independent predictor variables to the PCI result. To the best of our knowledge in all previous attempts, the authors have used regression-based methodological procedures to select the predictor variables that will be included in their models. In this study, a new approach is proposed to select the most important predictor variables. This approach is based on exploiting the dynamics of cause-effect relationships between variables. The focus was on the local causal structure around the target variable, particularly on its Markov blanket. The Markov blanket of the operation result was used as a feature selection tool towards developing the prediction model for the result of the PCI. Essentially, variables detected to be causally related with the result of the PCI were included as independent factors in a prediction model for the result of the PCI (success/failure). A similar approach has been already successfully applied in [9], aiming in detecting the most informative variables in lung cancer. The proposed prediction model, MBFS, included all five variables that constituted the Markov blanket of the operation result, namely, the CTO length, proximal tortuosity, stump, calcification, and segmental regional. The impact of proximal tortuosity, stump, calcification, and CTO length to the operation result (see Table 1) was found to be in agreement with the literature [2; 3; 4; 5; 6; 7]. Particularly, it is known that the proximal tortuosity categories moderate and severe are compounding regarding the success of the operation compared to the straight class (odds ratio values 0.671, 0.355, respectively). Similarly, the classes moderate and severe of calcification are compounding regarding the success of the operation compared to the none/mild class (odds ratio values 0.697, 0.395, respectively). The CTO length negatively impacted the operation success, exhibiting odds ratio equal to 0.986. On the other hand, the stump categories tapered and blunt positively impacted the operation success compared to the no category (odds ratio values 3.247, 1.946, respectively). Interestingly, a new predictor variable was included in this model, namely the segmental regional. It was found that the category representing hypokinetic/akinetic/dyskinetic  
Figure 4: Diagram of the area under the receiver operation characteristics (AUROC) curve of all six models, based on the test dataset (\(n=5210\)). was compounding for operation success, compared to the category representing normal segmental regional, exhibiting an odds ratio equal to 0.674 (Table 1). The MBFS model that is proposed in this study was assessed and compared to five well-known models from the literature. In the corresponding manuscripts of these five models [2; 3; 4; 5; 6], a multivariate binary logistic regression model was developed at first, and then a risk score was computed based on the magnitude of the coefficient estimates of the independent variables that were included in each model. Here, we were only interested in assessing the predictive ability of the binary logistic regression models themselves, which were all retrained in this dataset for fairness. Thus, the comparison is not a strict attempt to rank the MBFS and all five well-known models, but to also assess a novel approach that employs causality in detecting the most important predictor variables, compared to the standard approach that is mainly based on regression. The results of the assessment have not only shown that the MBFS model performed better than all its competitors, but a new important predictor factor emerged as well, namely, the segmental regional.