Establishment of a Cohort for Continuous Coronary Artery CTA Scanning and Predictive Analysis of Plaque Progression

Condition(s) studied

Study summary

This research develops risk prediction models for coronary artery stenosis and vulnerable plaques. The coronary artery stenosis model aims to predict stenosis using multimodal deep learning by integrating text, structured numerical data, and imaging features, focusing on metrics like maximum and cumulative stenosis. The vulnerable plaque model seeks to identify early formation indicators, allowing for timely interventions to prevent plaque rupture, using similar data integration techniques. Additionally, a decision support system is created, comprising a patient database, risk prediction models, and a high-risk alert module. This system facilitates real-time notifications to healthcare providers when risk thresholds are exceeded, enabling personalized treatment planning and improved patient outcomes.

Eligibility

Primary outcome measure(s)

• Maximum Coronary Stenosis (MAXS) — 2 year
  In 18 vascular segments, each segment is scored based on the degree of stenosis. The scoring criteria are as follows: no stenosis (0%) is scored as 0 points, mild stenosis (1%-24%) is scored as 1 point, moderate stenosis (25%-49%) is scored as 2 points, significant stenosis (50%-69%) is scored as 3 points, severe stenosis (70%-99%) is scored as 4 points, and total occlusion (100%) is scored as 5 points. MAXS represents the score of the single segment with the highest score among the 18 vascular segments.

Trial sites (1)

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