## 5 Model Evaluation
### Evaluation of State of the Art Models  
As described in the Background section, we compare our model to five state of the art models: (1) PRESTO-1 [20], (2) PRESTO-2 [11] both used by [PERSON] et al. in 2004, (3) EVENT [21] used by [PERSON] et al. in 2010, (4) Konigstein [15] used by [PERSON] et al. in 2019, and (5) GRACIA-3 [17] used by [PERSON] et al. in 2020. All these models were designed, trained, and evaluated slightly differently from each other. Here, we discuss how each of the models originally differed from our study and how we provided an equal test bed for the comparison of these models. In terms of method, PRESTO-1, PRESTO-2, and EVENT use logistic regression (LR) for prediction, Konigstein uses Cox proportional hazard, and GRACIA-3 has used a variety of ML classifiers (such as RF, ERT, LR) and concluded that the ERT classifier was superior. In terms of features used for training the models, in the original publications, all five models used slightly different feature sets. A summary of features used by these models is provided in Table 3 along with their overlap with our cohort features. In terms of outcomes of interest, PRESTO-1, PRESTO-2, and GRACIA-3 were designed for prediction of stent restenosis (SR). EVENT was designed for prediction of target lesion stent restenosis (TLR), and Konigstein was designed for TLF. These three outcomes are clinically closely related to each other. Our definition of TLF in this paper includes both TLR and SR. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** &
\begin{tabular}{} \end{tabular} & **Features** \\ \hline EVENT & LR & Age \textless{} 60 years, prior PCI, Left main PCI \({}^{\star}\), SVG location \({}^{\star}\), minimum stent diameter \(\leq\) 2.5 mm, total stent length \(\geq\) 40 mm \\ \hline PRESTO-1 & LR & Lesion length \textgreater{} 20 mm, ACC/AHA type C lesion, previous PCI, treated diabetes mellitus, non-smoker, vessel size (evaluated w.r.t. 3, 3.5, and 5 mm), unstable angina, gender \\ \hline PRESTO-2 & LR & Treated diabetes mellitus, non-smoker, vessel size \textless{} 3 mm, length of the lesion (evaluated w.r.t. 10 and 20 mm), ostial length \({}^{\star}\), previous PCI \\ \hline Konigstein & None & Stent length, moderate/severe calcification, post-procedural diameter stenosis, vessel diameter, hypertension, diabetes, prior coronary artery bypass grafting \({}^{\star}\), prior PCI \\ \hline GRACIA-3 & ERT & Demographic data (age, weight \({}^{\star}\), height \({}^{\star}\), systolic/diastolic blood pressure \({}^{\star}\), smoking, alcohol consumption \({}^{\star}\)), clinical data (diabetes mellitus, hypertension, dyslipidemia \({}^{\star}\), family history of cardiovascular diseases \({}^{\star}\), previous angina, previous MI, previous PCI), medications (ACE/RAA inhibitors \({}^{\star}\), betablockers \({}^{\star}\), calcium antagonists \({}^{\star}\), nitroglycerin \({}^{\star}\), aspirin \({}^{\star}\), clopidogrel), angiographic data (vessel disease, drug-eluting stent \({}^{\star}\), number of implanted stents, triofiban use \({}^{\star}\), satisfactory PCI result \({}^{\star}\), non-eflow \({}^{\star}\), pre- and post-PCI thrombus/TIMI flow/TMPG \({}^{\star}\)/minimal luminal diameter \({}^{\star}\)/percent stenosis diameter, percent area stenosis, lesion length), left ventricle function data (end diastolic/systolic volume \({}^{\star}\), ejection function \({}^{\star}\)), biochemical data (CK, CK-MB, total/LDL cholesterol \({}^{\star}\), platelets \({}^{\star}\), leucocytes \({}^{\star}\), hemoglobin \({}^{\star}\), hematocrit \({}^{\star}\), creatinine \({}^{\star}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features and models used in the original literature for each of the five state of the art models. \({}^{\star}\) = the feature is missing in our dataset and was excluded in the _retrained_ experiments. In terms of reporting model parameters, PRESTO-1, PRESTO-2, and EVENT report the LR model parameters (\(\beta\)-coefficients) after training on their own original database.