Bleeding, Coronary Artery Disease (CAD), Thrombosis
Conditions
Keywords
Coronary Heart Disease, Dual Antiplatelet Therapy, Thrombosis, Bleeding, Multi-Omics, Biomarkers, Risk Prediction Model, Machine Learning
Brief summary
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 better than existing ones at predicting who will develop clots or bleeding and at helping doctors decide on the best treatment for each patient. The new aspects of this research are: (1) using advanced multi-omics technology to find novel markers specifically for Chinese patients; (2) combining clinical, biological, and imaging data to improve prediction accuracy; and (3) using machine learning to create more precise risk models. The goal is to provide doctors with a more accurate tool to assess each patient's risk of clots and bleeding. This will help them choose the safest and most effective antiplatelet treatment, reduce serious complications, and improve patient care.
Interventions
None listed
Sponsors
Xueyan Zhao
The General Hospital of Northern Theater Command
Central China Fuwai Hospital of Zhengzhou University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥ 18 years. 2. Hospitalized due to symptoms or objective evidence of coronary heart disease and planned for long-term antithrombotic therapy. 3. Diagnosis of acute coronary syndrome or stable coronary heart disease undergoing percutaneous coronary intervention (PCI), with clinical stability meeting discharge criteria after treatment. 4. For patients from the existing cohort: minimum 2 years of follow-up with complete clinical data and blood specimens available; approval for use of these data and specimens has been obtained, with a waiver of re-consent. For newly enrolled patients: voluntary written informed consent provided by the patient or legal representative, agreement to provide blood samples, and acceptance of follow-up procedures.
Exclusion criteria
For patients from the existing cohort: 1. Severe missing or erroneous baseline or clinical data that cannot be corrected by source verification. 2. No available blood specimen, or specimen that does not meet testing requirements. For newly enrolled patients: 1. Presence of serious comorbid conditions with life expectancy ≤ 6 months. 2. Conditions that significantly affect study compliance or the ability to complete follow-up. 3. Contraindications to blood sampling.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) | Up to 24 months after enrollment | Composite of all-cause death, non-fatal myocardial infarction, ischemic stroke, and unplanned revascularization. |
| Major Bleeding Events | Up to 24 months after enrollment | Bleeding Academic Research Consortium (BARC) type 3 or type 5 bleeding. |
Secondary
| Measure | Time frame |
|---|---|
| All-Cause Death | Up to 24 months after enrollment |
| Myocardial Infarction | Up to 24 months after enrollment |
| Stroke | Up to 24 months after enrollment |
| Stent Thrombosis | Up to 24 months after enrollment |
Countries
China
Contacts
CONTACTXueyan Zhao, PhD
Outcome results
None listed