Predicting how coronary artery disease progresses in adults 45 and younger
Official title: Risk Prediction of Disease Progression in Early-Onset Coronary Atherosclerosis
Run by Fu Wai Hospital, Beijing, China · NCT07316452 · Data updated 2026-11-12
This project will try to build prediction models using coronary CT scans and health data from people aged 18–45 to see who is more likely to have plaque get worse over time.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 45 Years |
| Sex | All |
| Enrollment | 10000 (estimated) |
| Sponsor | Fu Wai Hospital, Beijing, China Academic / other |
| Locations | 1 site (Beijing) |
| Status | Recruiting |
| Started | January 1, 2025 |
| Main results expected | September 1, 2027 |
| Study ends | January 1, 2028 |
| Registry record updated | January 5, 2026 |
| Trial ID | NCT07316452 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18–45 with symptoms suggestive of coronary artery disease who have completed a coronary CT, can provide informed consent, and are willing to attend follow-up visits are ideal candidates.
✕Probably not a fit
People with prior coronary revascularization, poor‑quality coronary CT images, severe life‑limiting systemic disease, pregnant or breastfeeding women, or those unable to complete follow‑up are unlikely to gain benefit from participation.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a large, multicenter observational effort across China enrolling about 10,000 participants aged 18–45 who undergo coronary CT. At enrollment the project collects basic health information, cardiovascular risk factors, blood test results, medication use, lifestyle habits, and optional genetic data for some participants. Participants will be asked to return for follow-up coronary CT scans after several years to measure changes in plaque and disease progression. The collected clinical and imaging data will be used to identify factors linked to progression and to build models that estimate future plaque progression and cardiovascular event risk.
Why it matters
Potential benefit: If successful, the models could help clinicians identify younger adults at higher risk so they can offer earlier prevention and monitoring to reduce future heart events.
How similar studies have gone: Previous observational cohorts have linked clinical risk factors and CT plaque features to progression, but large nationwide CT‑based prediction efforts specifically focused on young adults are relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Age between 18 and 45 years at the time of enrollment. 2. Presence of symptoms suggestive of coronary artery disease and completion of coronary computed tomography (CT) examination. 3. Ability and willingness to provide written informed consent and to participate in follow-up assessments. Exclusion Criteria: 1. Inability or unwillingness to complete follow-up assessments. 2. Inability to provide written informed consent. 3. History of coronary revascularization, including percutaneous coronary intervention or coronary artery bypass grafting. 4. Poor-quality coronary computed tomography images that are inadequate for reliable plaque assessment. 5. Presence of severe systemic disease with limited life expectancy that may interfere with study follow-up. 6. Pregnant or breastfeeding women. 7. Participation in another clinical study that may interfere with the outcomes of this study.
Where it's running
-
FuWai hospital
Beijing, China Recruiting
Study contacts
- Study coordinator: Zhihui Hou, MD, PhD
- Email: konglingyihui@126.com
- Phone: +86 15011451210
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.