## 3 Results  
The variable characteristics of the training and test datasets are shown in Supplemental Tables 1 and 2 for all the 21 variables considered, separately for the cases with successful operation and unsuccessful operation. The results show that the training and test datasets are homogeneous regarding all variables considered. For completeness, the variable characteristics are shown as well for the downsampled training dataset (Supplemental Table 1), where the results are, as expected, different in general compared to the other two datasets. The causal graph developed based on the PC algorithm is displayed in Figure 2. The graph revealed the complicated network structure of causal relations among factors that have effect on the operation result. Five variables appear to have a cause-effect relationship with the operation result, particularly the CTO length, proximal tortuosity, stump, calcification, and segmental regional are all direct causes of the operation result. The operation result does not have any effects/children in the graph; thus, the Markov blanket of the operation result is composed from its causes alone. The five variables included in the Markov blanket were then included in a multivariate binary logistic regression model, and the results are shown in Table 1. All five variables were statistically significant in the multivariate model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**95\% Confidence Interval for Exp (B)**} \\ \hline
**Variables** & **Exp (B)** & **Lower** & **Upper** & \(p\)**-Value** \\ \hline (Intercept) & 1.501 & 1.17 & 1.93 & 0.001 \\ \hline \multicolumn{4}{c}{Calcification\_None/Mild (spots)} & 1.000 \\ \hline \multicolumn{4}{c}{Calcification\_Moderate (\(<\)=50\% RLD)} & 0.697 & 0.58 & 0.84 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Calcification\_Severe (>50\% RLD)} & 0.395 & 0.32 & 0.49 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Segmental regional\_Normal} & 1.000 \\ \hline \multicolumn{4}{c}{Segmental regional\_H/A/D} & 0.674 & 0.58 & 0.79 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{CTO length} & 0.986 & 0.98 & 0.99 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Proximal tortuosity\_Straight} & 1.000 \\ \hline \multicolumn{4}{c}{Proximal tortuosity\_Moderate} & 0.671 & 0.56 & 0.80 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Proximal tortuosity\_Severe} & 0.355 & 0.27 & 0.47 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Stump\_No} & 1.000 \\ \hline \multicolumn{4}{c}{Stump\_Tapered} & 3.237 & 2.58 & 4.07 & \textless{}0.001 \\ \hline \multicolumn{4}{c}{Stump\_Blunt} & 1.946 & 1.56 & 2.43 & \textless{}0.001 \\ \hline \end{tabular}
\end{table} 
Table 1: Multivariate binary logistic regression analysis for success of the PCI. Estimates of the odds ratios (Exp(B)) and their 95% confidence intervals corresponding to the MBFS model based on the downsampled training dataset (\(n\) = 3026) are displayed, along with their \(p\)-values. Figure 2: The causal graph over the downsampled training dataset (\(n\) = 3026, 22 variables) is displayed. The five nodes/variables constituting the Markov blanket around the operation success exhibit a directed arrow towards operation success. In Table 2, the assessment results of the MBFS model are shown. The overall accuracy of the MBFS model at a prediction probability threshold of 0.5 was 66.47%, with sensitivity at 66.62% (representing the percentage of correctly identifying cases with successful operation), and specificity at 65.43% (representing the percentage of correctly identifying cases with unsuccessful operation). The AUROC was 0.7145. Figure 3 shows the tradeoff between sensitivity, specificity, Youden index (sum of sensitivity and specificity minus one), and misclassification error for different prediction probability thresholds. It is displayed that at the standard threshold of 0.5, the curves corresponding to sensitivity and specificity almost cross each other, thus resulting in very similar values (66.62% and 65.43%).