Developing an AI System to Predict and Manage Coronary Artery Lesion Progression After Percutaneous Coronary Intervention in China

What this Trial Is About

Coronary heart disease is a major cause of death worldwide, and while percutaneous coronary intervention PCI helps improve outcomes, patients still face significant long-term risks from the progression of non-target lesions NTLs. Current tools are not enough to predict which patients will experience this progression. This multicenter cohort study in China aims to develop an artificial intelligence AI system that uses clinical data, blood biomarkers, and coronary imaging from over 52,000 patients to better predict and stratify the risk of NTL progression after PCI. The goal is to enhance personalized management and improve long-term prognosis. The study collects comprehensive multimodal data, including clinical profiles, multi-omics blood biomarkers, and coronary imaging, from patients who are scheduled for or have undergone PCI. Researchers will identify specific biomarkers linked to NTL progression and use machine learning methods to build a risk assessment and early-warning system. The study also plans to classify patients into metabolic and imaging-based subtypes to guide precision management and secondary prevention strategies. Participants will provide baseline plasma samples and agree to follow-up assessments over 12 months. The research team will monitor outcomes such as myocardial infarction or the need for revascularization related to NTL progression using these data. The studys primary outcome focuses on a composite endpoint of NTL progression-related events, with secondary measures including qualitative and quantitative data on lesion progression. This observational study involves no experimental treatment but closely tracks patient health and disease progression to inform future risk prediction tools.

AI-Driven Dynamic Prediction of Non-Target Lesion Progression After PCI: A Chinese Multicenter Cohort Study