Percutaneous coronary intervention (PCI) is a cornerstone treatment for coronary artery disease, yet accurate prediction of long-term mortality remains a critical challenge due to the complex interplay of risk factors.
We present a novel multimodal machine learning framework that integrates coronary angiography video, unstructured procedural text, and structured clinical variables to predict 5-year all-cause mortality. Utilizing a large real-world cohort of 10,353 patients. Our trimodal LightGBM model achieved an AUC-ROC of 0.814, significantly outperforming single- and dual-modality baselines. The unstructured data captured complementary prognostic signals, while structured variables provided concentrated predictive strength.
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