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ID06001073

Predicting Outcomes in Patients with Cardiovascular and Cerebrovascular Diseases Using Multi-omics Data A National, Multicenter, Retrospective and Prospective Cohort Study

Led by First Affiliated Hospital Xi'an Jiaotong University · Updated on 2024-07-12

25000

Participants Needed

1

Research Sites

26 weeks

Total Duration

AI-Summary

What this Trial Is About

Cardiovascular and cerebrovascular diseases, including coronary atherosclerosis, cardiomyopathy, atrial fibrillation, and stroke, remain major public health challenges with unclear causes and disease processes. This research aims to collect detailed clinical and biological data from patients with these conditions to better understand their underlying mechanisms. Using multi-omics technology, the study seeks to develop early prediction models and offer new approaches for personalized treatment of these diseases. Participants diagnosed with various cardiovascular or cerebrovascular diseases according to clinical guidelines, such as coronary artery disease, arrhythmia, heart valve disease, aortic dissection, cardiac masses, myocarditis, hypertension, cardiomyopathy, structural heart disease, ischemic or hemorrhagic cerebrovascular disease, and intracranial space occupying lesions, will be included. This observational study does not involve administering treatments but monitors patients over time to gather comprehensive data. During the study, participants will be regularly followed up with clinical visits and data collection, including biological specimens. Researchers will observe changes in major adverse cardiovascular and cerebrovascular events MACCE over up to five years, along with secondary outcomes like all-cause mortality, myocardial infarction, coronary revascularization, and stroke incidence. The study supports long-term monitoring to improve understanding and treatment strategies for these diseases.

CONDITIONS

Brief Title

Prognosis Prediction System of Patients With Cardiovascular and Cerebrovascular Diseases Based on Multi-omics

Research Team

G

Gouliang Li

Y

Yang Yan

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