## 6 Results and Discussion
### Performance of the State of the Art Models
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Variables** & **EVENT** & \multicolumn{3}{c}{**PRESTO-1**} & \multicolumn{3}{c}{**PRESTO-2**} \\ \hline  & _Test-Only_ & _Retrained_ & _Test-Only_ & _Retrained_ & _Test-Only_ & _Retrained_ \\ \hline Patient age \textless{} 60 y & 0.401 & 0.143 & - & - & - & - \\ Left main PCI & 1.144 & NA & - & - & - & - \\ SVG location & 0.876 & NA & - & - & - & - \\ Minimum stent diameter \(\leq\) 2.5 mm & 0.430 & 0.175 & - & - & - & - \\ Total stent length \(\geq\) 40 mm & 0.577 & 0.911 & - & - & - & - \\ Prior (previous) PCI & 0.604 & \(-\)0.075 & 0.344 & 0.048 & - & - \\ ACC/AHA type C lesion & - & - & 0.593 & \(-\)0.011 & - & - \\ Treated diabetes mellitus & - & - & 0.344 & 0.146 & 0.372 & 0.241 \\ Unstable angina & - & - & 0.174 & 0.327 & - & - \\ Female gender & - & - & 0.140 & \(-\)0.460 & - & - \\ Non-smoker & - & - & 0.329 & \(-\)0.278 & 0.293 & 0.493 \\ Otsial length & - & - & - & - & 0.600 & NA \\ Lesion length \(\geq\) 20 mm & - & - & 0.728 & \(-\)0.005 & 0.859 & 0.050 \\ Vessel size & & & & & & \\ \(\leq\)3 mm & - & - & 0.565 & 0.321 & 0.278 & 0.014 \\
3-3.5 mm & - & - & 0.365 & 0.266 & - & - \\
3.5–4 mm & - & - & 0.166 & 0.0178 & - & - \\ \(>\)4 mm & - & - & 0.000 & 0.000 & - & - \\ \hline Model intercept & - & 0.093 & - & 0.048 & - & \(-\)0.580 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The model parameter values (\(\beta\)-coefficients for logistic regression-based models) as were reported in the literature by authors (_test-only_ columns), and after retraining on our database (_retrained_ columns); PRESTO-1 [20], PRESTO-2 [11], EVENT [21]. The outcome of interest here is TLR, similar to the original studies. The last row indicates the logistic regression model intercept, only reported for retrained models, and not reported by the authors. NA means the parameter did not exist in our database; - means the parameter did not exist in the original model. parameters or by retraining the model on our dataset, respectively. Since the articles about the Konigstein and GRACIA-3 models do not provide direct pre-trained models, we have denoted them as NA in the _test-only_ results. In addition, the original Konigstein model only includes hazard ratio values for a set of variables and does not include a predictive model. To report _retrained_ for Konigstein, we used their set of features, trained multiple predictive models (with nine conventional techniques), and reported the performance of the best model (random forest). As shown in Table 6, the performances of all models slightly improved from _test-only_ to _retrained_, reflecting the fact that models pre-trained on original cohorts might not give the best results when tested on a different cohort. Furthermore, EVENT, PRESTO-1, PRESTO-2, Konigstein, and GRACIA-3 models did not reach the same level of performance in the _retrained_ step as reported in their respective original studies which again points to the difference among the collected features between cohorts. Our SL model outperform all other state of the art models. However, GRACIA-3 has again outperformed the other four models both in TLF and TLR prediction, similar to the report in their original paper. This can be explained by larger number of features that was used in this model (Table 3). For the same reason, GRACIA-3 obtained the closest level of performance to our SL model. The performances for TLR and TLF predictions among all of the models are close to each other, reflecting the high degree of correlation between these two outcomes. Nevertheless, it is surprising that among the first three models, the _test-only_ precision scores for TLF are higher than those of TLR, even though the models have been proposed for predicting SR and TLR outcomes. This difference might not be statistically significant and simply due to variability in our database. As a summary, the better performance of our model compared to other models was expected, as our database contains more features and we designed a more detailed model.