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Patients with coronary heart disease who take dual antiplatelet therapy face two serious risks: thrombosis and major bleeding. This study aims to develop better ways to predict these risks and guide personalized treatment. The investigators will use a large, long-term follow-up study of Chinese patients with coronary heart disease. This research plans to discover new biomarkers related to clot and bleeding risk. The study will combine information from proteins, metabolites, sugars attached to proteins, genes, and medical images. Using machine learning methods, the investigators will identify the most important markers and test them in the patient group of this study. The investigators will then build new risk prediction models that include these new markers together with traditional risk scores (such as GRACE, PARIS, and Precise-DAPT). This study will check whether these new models are
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