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Quantification of Coronary Atherosclerosis in the Assessment of Coronary Artery Disease
Sang-Eun Lee1,2, Ji Min Sung1,2, Asim Rizvi3
1Division of Cardiology, Severance Cardiovascular Hospital (H.-J.C., S.-E.L., J.M.S.).
Insights
Quantifying coronary plaque burden (PB) using computed tomography angiography improves prediction of coronary artery disease progression. Direct PB measurement offers better risk stratification than traditional stenosis assessment alone.
Area of Science:
- Cardiovascular Imaging
- Interventional Cardiology
- Radiology
Background:
- Traditional diagnosis of coronary artery disease relies on diameter stenosis ≥50%.
- The predictive value of direct plaque burden (PB) and plaque characteristics for rapid progression is under-explored.
- Coronary computed tomography angiography (CCTA) enables quantitative plaque assessment.
Purpose of the Study:
- To evaluate if direct quantification of PB and plaque characteristics by CCTA adds value in predicting rapid plaque progression.
- To compare the predictive performance of quantitative PB analysis versus conventional angiography for coronary artery disease (CAD).
- To assess the utility of PB quantification for improving CAD risk stratification.
Main Methods:
- Prospective observational registry with 1345 patients from 13 centers across 7 countries.
- Repeated CCTA performed >2 years apart for quantitative analysis of PB and annual change (△PB/y).
- Conventional angiographic analysis included stenosis ≥50%, vessel involvement, segment involvement score, and high-risk plaque features.
Main Results:
- Rapid progressors (≥median △PB/y) were older, more often male, and had more risk factors.
- Baseline PB improved prediction of rapid progression, further enhanced by high-risk plaque features (P<0.001).
- Combined baseline PB and △PB/y provided the best prediction of adverse outcomes (C-statistic 0.763, P<0.001).
Conclusions:
- Direct quantification of atherosclerotic PB by CCTA offers incremental value for risk stratification in coronary artery disease.
- Quantitative plaque assessment may enhance the prediction of disease progression and adverse clinical outcomes.
- Integrating PB quantification into routine assessment could improve patient management strategies for CAD.
Background:
Diagnosis of coronary artery disease and management strategies have relied solely on the presence of diameter stenosis ≥50%. We assessed whether direct quantification of plaque burden (PB) and plaque characteristics assessed by coronary computed tomography angiography could provide additional value in terms of predicting rapid plaque progression.
Methods And Results:
From a 13-center, 7-country prospective observational registry, 1345 patients (60.4±9.4 years old; 57.1% male) who underwent repeated coronary computed tomography angiography >2 years apart were enrolled. For conventional angiographic analysis, the presence of stenosis ≥50%, number of vessel involved, segment involvement score, and the presence of high-risk plaque feature were determined. For quantitative analyses, PB and annual change in PB (△PB/y) in the entire coronary tree were assessed. Clinical outcomes (cardiac death, nonfatal myocardial infarction, and coronary revascularization) were recorded. Rapid progressors, defined as a patient with ≥median value of △PB/y (0.33%/y), were older, more frequently male, and had more clinical risk factors than nonrapid progressors (all P<0.05). After risk adjustment, addition of baseline PB improved prediction of rapid progression to each angiographic assessment of coronary artery disease, and the presence of high-risk plaque further improved the predictive performance (all P<0.001). For prediction of adverse outcomes, adding both baseline PB and △PB/y showed best predictive performance (C statistics, 0.763; P<0.001).
Conclusions:
Direct quantification of atherosclerotic PB in addition to conventional angiographic assessment of coronary artery disease might be beneficial for improving risk stratification of coronary artery disease.
Clinical Trial Registration:
URL: https://www.clinicaltrials.gov. Unique identifier: NCT02803411.
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