Abstract Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients.

Simulation-based inference (SBI) of latent parameters in physical systems is often hindered by model misspecification--the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, a recent SBI approach, addresses this challenge through a two-stage domain transfer process that combines semi-supervised calibration with optimal transport (OT)-based distribution alignment. However, RoPE operates in a fully transductive setting, requiring access to a batch of test samples at inference time, which limits scalability and generalization. We propose a fully inductive and amortized SBI framework that integrates calibration and distributional alignment into a single, end-to-end trainable model. Our method leverages mini-batch OT with a closed-form coupling to align real and simulated observations that correspond to the same latent parameters, using both paired calibration data and unpaired samples. A conditional normalizing flow is then trained to approximate the OT-induced posterior, enabling efficient inference without simulation access at test time. Across a range of synthetic and real-world benchmarks--including complex medical biomarker estimation--our approach matches or exceeds the performance of RoPE, while offering improved scalability and applicability in challenging, misspecified environments.