## 7 Conclusions  
In this paper, we presented a machine learning (ML) model to predict TLF using information available before patient discharge. We used our BIOSOLVE-IV post-market study dataset (September 2016-2025) including data of 1975 patients located across the world. These patients had received the Magmaris sirolimus-eluting bioresorbable stent (Biotronik AG, Bulach, Switzerland). For each patient, a total of 86 variables were measured, composed of information before their surgery until after discharge. Additionally, regular follow-up information for up to five years after discharge was collected. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**TLR Prediction**} & \multicolumn{2}{c}{**TLF Prediction**} \\ \hline  & & _Test-Only_ & _Retrain_ & _Test-Only_ & _Retrain_ \\ \hline \multirow{2}{*}{EVENT} & AUC-ROC & 0.51 \(\pm\) 0.01 & 0.54 \(\pm\) 0.06 & 0.51 \(\pm\) 0.01 & 0.54 \(\pm\) 0.06 \\  & Precision & 0.062 & 0.066 & 0.076 & 0.081 \\ \hline \multirow{2}{*}{PRESTO-1} & AUC-ROC & 0.51 \(\pm\) 0.01 & 0.55 \(\pm\) 0.05 & 0.51 \(\pm\) 0.01 & 0.56 \(\pm\) 0.03 \\  & Precision & 0.065 & 0.070 & 0.079 & 0.083 \\ \hline \multirow{2}{*}{PRESTO-2} & AUC-ROC & 0.52 \(\pm\) 0.01 & 0.54 \(\pm\) 0.07 & 0.51 \(\pm\) 0.01 & 0.52 \(\pm\) 0.02 \\  & Precision & 0.063 & 0.066 & 0.077 & 0.077 \\ \hline \multirow{2}{*}{Konigstein} & AUC-ROC & 0.54 \(\pm\) 0.07 & 0.49 \(\pm\) 0.06 \\  & Precision & NA & 0.064 & NA & 0.074 \\ \hline \multirow{2}{*}{GRACIA-3} & AUC-ROC & 0.61 \(\pm\) 0.06 & NA & 0.58 \(\pm\) 0.03 \\  & Precision & 0.072 & 0.091 \\ \hline \multirow{2}{*}{SL (early-term)} & AUC-ROC & 0.64 \(\pm\) 0.02 & 0.62 \(\pm\) 0.01 & NA & 0.62 \(\pm\) 0.01 \\  & Precision & 0.067 & 0.077 & 0.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of performance between our model (SL early-term), and five of the state of the art models. Reported values are AUC-ROC and their standard deviation, as well as precision score for the model when it was set to achieve 90% sensitivity. NA means models do not exist. Our model consists of three components: (1) TLF prediction at discharge via combination of nine ML models and a super-learner, (2) prediction of a risk score for time to TLF, and (3) updating the risk score for at-risk patients during their follow-ups. We train and evaluate our models, both retrospectively and prospectively, at three times points: discharge, end of Phase I (early-term prediction), three months after Phase I (late-term prediction), and six months after Phase I (very late-term prediction). The five main challenges of this problem are: (1) it is clinically extremely difficult to predict TLF, (2) it is important to optimize between the tool's sensitivity and the size of the at-risk cohort, (3) unbalanced class sizes hamper training, (4) missingness in the outcome introduces bias in inference, and (5) cohort selection bias exists due to restrictive inclusion criteria in our clinical study. Our main contributions in this paper are: (1) the development of a novel ML method for the identification of patients at risk of TLF. This model can predict the onset of TLF at any time point after discharge from hospital. To the best of our knowledge, such an analysis on TLF is the first of its kind. (2) Training and validation are performed with our international, multi-site cohort, with comprehensive variables collected during treatment and 5 years of frequent follow-ups. Data were collected from 120 medical centers in over 25 countries across the globe. (3) We evaluate our model against five state of the art models via multiple experiments. (4) We demonstrate a successful retrospective and prospective evaluation of our model in three time frames (early-, late-, and very late-term) while maintaining a reasonable at-risk cohort size. Our algorithm successfully predicts TLF for 86 patients (TPR = 52.76%) (82 in early term, two in late term, and two in very late term). It identifies 1635 patients as not at risk (TNR including right-censored patients = 90.23%).