## 4 Methodology
### TLF Prediction Component  
The TLF prediction model component encompasses nine ML prediction models along with the pre-processing steps required for resolving missingness, adjusting sample size, reducing dimensionality, and normalizing the data, as well as a complete nested cross-validation setup. The pre-processing steps are designed to be optionally turned on and off during the model selection phase. A large part of the inconsistency and lack of produciblity in the ML literature comes from hidden pre-processing steps. In the following section, we explain how incorrect execution of these pre-processing steps introduces bias in the final results and findings of any ML models. (1) Transformation: To correct for differences in feature scales and outliers, we employ a scaling step in order to prevent convergence issues and unreliable model training. Since the straightforward normalization cannot be applied to missing data, the Quantile Transformer can be considered a possible alternative. First, it maps the feature values to a uniform distribution using the feature's empirical cumulative distribution function (eCDF), and then maps the transformed values to a target distribution by applying the associated quantile function. This operation is monotone and rank-preserving. The main benefit of this technique is alleviating the effect of outliers by scaling them into a sensible \begin{table}
\begin{tabular}{p{85.4 pt} p{341.4 pt} p{341.4 pt}} \hline \hline
**Timeline** & **Supplementary References** & **Features** \\ \hline Pre-intervention & Table S2 & Demographic information (age, gender), EQ5D questionnaire (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), MI information (prior MI, type of most recent MI), prior stroke/TIA, diseases and complications (renal, hepatic, respiratory, hypertension, hypercholesterolemia, diabetes mellitus, congestive heart failure), history of cancer, number of prior PCIs, ischemic status (STEMI, NSTEMI, CCS class of stable angina, unstable angina, silent ischemia, LVEF class) \\ \hline Intra-operation & Table S3 & Procedure details (duration, residual stenosis before implantation), device (Magmaris scaffold) details (number of implanted devices, maximum pressure applied, residual stenosis after implantation, device deficiency prior to/during procedure), pre-dilatation balloon details (number of balloons, diameter, length, number of inflations, maximum pressure), post-dilatation balloon details (same as for pre-dilatation) \\ \hline Lesion and Stent & Table S4 & Lesion information (location, ACC/AHA characterization, moderate/severe calcification, eccentric lesions, length), vessel information (location, moderate/severe angulation, moderate/excessive tortuosity, reference diameter), pre-procedure TIMI flow, bifurcation, thrombus, stenosis pre-procedure \\ \hline Medications & Table S5 & ASA (prior to procedure, loading dose), heparin (bolus injection prior to procedure, during procedure), anti-platelet medication (prior to procedure, loading dose) \\ \hline Discharge Information & Table S6 & Troponin (if clinically significant, if out of normal range), ischemic status (CCS class of stable angina, unstable angina, silent ischemia) \\ \hline Follow-up & Figure S1 & TLF defined as combination of TLR, MI, CABG, and ST \\ \hline \hline \end{tabular}
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Table 2: Summary of 86 features used in our study, grouped by the timeline of treatment for each individual patient. range and dispersing extremely close values which will potentially lead to better model training. However, this means that the in-between sample correlations do not remain the same, of which we should be aware. (2) Imputation: We performed imputation to deal with missingness in the dataset. We opted to use conventional imputation over more stable approaches such as inverse-probability weighting [31] as no particular parameter was of higher interest a priori and the data were to be used as input for various downstream classifiers.