## 5 Conclusions  
The above discussion highlights the strong potential of causality as a feature selection tool in prediction model development. Particularly, capitalizing on the dynamics of local causal structure and the Markov blanket resulted in a promising prediction model, and showcased the prospects of employing causal relationships in building prediction models. Causal model development has yet to be thoroughly assessed as a prediction tool in different scientific fields. In this study, the usage of local causal structure as a feature selection tool for prediction regarding the result of PCI in CTO resulted in developing a very competitive model, thus, highlighting the potential of causal models for applications. Conceptualization, M.G., G.S. and L.A.; data curation, M.G. ; methodology, M.G. and L.A.; software, M.G. and L.A.; validation, G.S. and L.A.; formal analysis, M.G. and L.A.; resources, G.S. ; writing--original draft preparation, M.G. and I.K. ; writing--review and editing, M.G., G.S. and L.A.; visualization, M.G. ; supervision, G.S. and L.A.; project administration, M.G. ; funding acquisition, M.G., I.K., G.S. and L.A. All authors have read and agreed to the published version of the manuscript. This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme \"Support for Researchers with Emphasis on Young Researchers\" in the context of the project 'EDBM103-Part B, 2020-2022' (MIS: 5047860). The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of each center. Informed Consent Statement:Informed consent was obtained from all subjects involved in the study. The data will not be made available to other researchers for purposes of reproducing the results. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Variable**}} & **Operation Success** & **Operation Failure** \\  & \(n\) = **10,647** & **m = 1513** \\ \hline \hline \multicolumn{1}{l}{Calcification (\%)} & 6270 (58.9) & 583 (38.5) \\ \multicolumn{1}{l}{None/Mild (spots)} & 2877 (27.0) & 464 (30.7) \\ \multicolumn{1}{l}{Moderate (\(<\)=50\% RLD)} & 1500 (14.1) & 466 (30.8) \\ \multicolumn{1}{l}{Severe (\(>\)50\% RLD)} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{Segmental regional (\%)} & 5658 (53.1) & 678 (44.8) \\ \multicolumn{1}{l}{Normal} & 4989 (46.9) & 835 (55.2) \\ \multicolumn{1}{l}{H/A/D} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{Age, mean (SD)} & 64.06 (10.7) & 65.21 (10.1) \\ \hline \hline \multicolumn{1}{l}{Weight, mean (SD)} & 84.32 (16.3) & 84.47 (16.4) \\ \hline \hline \multicolumn{1}{l}{Family history (\%)} & 7379 (69.3) & 1065 (70.4) \\ \multicolumn{1}{l}{No} & 3268 (30.7) & 448 (29.6) \\ \multicolumn{1}{l}{Yes} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{Dyslipidemia (\%)} & 2356 (22.1) & 303 (20.0) \\ \multicolumn{1}{l}{No} & 8291 (77.9) & 1210 (80.0) \\ \multicolumn{1}{l}{Yes} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{Diabetes (\%)} & 7382 (69.3) & 1025 (67.8) \\ \multicolumn{1}{l}{No} & 3265 (30.7) & 488 (32.3) \\ \multicolumn{1}{l}{Yes} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{Peripheral disease (\%)} & 9611 (90.3) & 1314 (88.8) \\ \multicolumn{1}{l}{No} & 1036 (9.7) & 199 (13.2) \\ \multicolumn{1}{l}{Yes} & \multicolumn{1}{l}{} \\ \hline \hline \multicolumn{1}{l}{COPD (\%)} & 10108 (94.9) & 1411 (93.3) \\ \multicolumn{1}{l}{No} & 539 (5.1) & 102 (6.7) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive characteristics for the training dataset (_n_ = 12,160). For quantitative variables, the mean and standard deviation (in brackets) are displayed. For qualitative variables, the frequencies and corresponding percentages (in brackets) are displayed for each category.