## 2 Background  
Early prediction of TLF via analysis of large cohorts can help improve patient outcomes and optimize their follow-up care. Tables 1 and S1 summarize the state of the art in this field. The first risk prediction methods for coronary artery disease were developed in 2004 based on multivariate regression [11]. More recently, guidelines were established for the prediction of adverse events for coronary artery disease patients, such as the Framingham Risk Score in 2010 [12] or the Systematic Coronary Risk Evaluation algorithm in 2016 [13]. Apart from these, there also exist statistical methods on possible risk factors for TLF for various stent technologies [14; 15]. Those algorithms are based on multivariate regression incorporating well-known risk factors, such as diabetes, smoking habit, and age, alongside their in-between interactions available from prior knowledge. In general, they exhibit only modest predictive power due to relatively strong model assumptions. A few existing machine learning (ML) approaches have been applied [16; 17] for TLF prediction to date. The benefit of ML methods compared to previously used methods is that they can search large amounts of data and identify multi-variable patterns that reliably predict outcomes of interest [18; 19]. However, the existing studies for TLF prediction suffer from the following issues: (1) there exist only simple state models of hand-picked variables, (2) the sizes of the cohorts are limited and they are mostly from single-site hospitals, (3) comprehensive observations about patients are lacking, and (4) the observations are rarely longitudinal, without longer-term follow-up. In this paper, we aim to identify the post-intervention TLF outcome, months before its onset, by using a novel ML approach and an international cohort. This may allow physicians to identify at-risk patients much earlier and to prioritize their follow-up care. We compare our model to five state of the art models: (1) PRESTO-1 [20], (2) PRESTO-2 [11] both conducted by [PERSON] et al. in 2004, (3) EVENT [21] conducted by [PERSON] et al. in 2010, (4) Konigstein [15] conducted by [PERSON] et al. in 2019, and (5) GRACIA-3 [17] conducted by [PERSON] et al. in 2020. The first three models are used extensively in clinical practice. They were originally designed to predict the risk of developing stent restenosis (PRESTO-1, PRESTO-2) or TLR (EVENT) for post-intervention patients. The fourth model, Konigstein, was originally designed to predict post-intervention TLF using a univariate Cox proportional hazard model, and the fifth is a recent ML-based model trained on the GRACIA-3 cohort for the prediction of stent restenosis. All these models have a large overlap in their features with our database and involve similar cohorts, and hence are included in our comparison. We excluded other state of the art methods in our comparison because of the following two reasons: (1) there [5; 14; 22] was little to no overlap between their respective feature sets and ours; for example, these methods are mainly based on features extracted from CT images or angiography. Such features could not be routinely collected in follow-ups of our multi-national cohort, not least due to resource disparities between the 25 participating countries. (2) They were designed to study cardiac risk factors in a rather stable or healthy population [5; 12; 16], leading to the exclusion of patients with previous histories of stent intervention, who, however, comprise the entirety of our cohort. Therefore, a head-to-head comparison is not meaningful.