## 1 Introduction  
Cardiovascular disease (including coronary artery disease) is regarded as the most severe and lethal disease in humans. According to data provided by the WHO, it causes one third of all deaths globally (approximately 17.9 million p.a.) [1]. In coronary artery disease, the arteries of the heart are blocked or narrowed from a buildup of cholesterol and fatty substances. This obstruction reduces the blood flow to the heart or forms a blood clot, leading to a heart attack. Existing guidelines [2] recommend coronary stents as the default means of treatment. Stent interventions are routine procedures; the stent is inserted into the affected vessel, expanded, and left inside the body permanently. More than four million stents are inserted annually worldwide [3] (incl. 29,000 in Switzerland, 25,000 in Austria, and 379,000 in Germany [4]). There are guidelines and risk score models for the prediction of general cardiac events for stable or at-risk patients. These models use a variety of features such as clinical parameters, computed tomography (CT) and magnetic resonance imaging (MRI), and radiomics [5, 6, 7] to segment the structure [6, 8] of the cardiovascular system, extract quantitative features from medical images (Radiomics), diagnose the potential risk, and predict a future adverse event (e.g. myocardial infarction). These models focus on understanding the risk factors for relatively stable populations, before they require surgical treatment. In contrast, in our study, we focus on the development of adverse events after patients receive stent implantation treatment. Figure 1 highlights the focus of our investigation and how it complements existing guidelines. In the remainder of the paper, the outcome of interest is post-intervention target lesion failure (TLF). TLF is defined as a composite of multiple clinical endpoints: cardiovascular death, clinically driven target lesion revascularization, and target vessel myocardial infarction [9]. Another clinically relevant endpoint is stent/scaffold thrombosis. For a better overview and readability, scaffold thrombosis are also be assigned to the TLF category throughout this paper. In this study, we aim to predict TLF as an adverse event after a stent surgery by using a novel ML approach and an international cohort. This may allow physicians to identify at-risk patients earlier, and to prioritize resources for follow-up care. Our patient data were collected in the Biotronik BIOSOLVE-IV post-market study (2016-2025) [10], a prospective, single-arm, multi-center, open-label registry which aims to investigate the clinical performance and long-term safety of the bioresorbable stent Magmaris in a real-world setting. The study has been approved by the relevant ethics committees and competent authorities, and all patients have provided written informed consent. The five main challenges for building an early prediction system for TLF are: (1) there are no clinically accepted bio-markers (features) for identification of at-risk patients. (2) As a decision support tool, it is important to optimize between the tool's sensitivity and the size of the at-risk cohort. (3) Model training is hampered by unbalanced class sizes (only 7.6% patients experienced TLF). (4) Missingness in the observations (nested input observations or right-censoring of the outcome) needs to be handled appropriately to avoid bias in inference. (5) Clinical studies usually suffer from selection bias due to restrictive cohort inclusion criteria. Our main contributions in this paper are as follows:  
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.