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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Natural History of Adapted Leaman Score Assessing Coronary Artery Disease Progression by Computed Tomography
Alexandre Hideo-Kajita1, Hector M Garcia-Garcia1, Rodrigo Rubarth2
1MedStar Washington Hospital Center, Interventional Cardiology Department, USA.
Background:
Coronary computed tomographic angiography (CCTA) provides a non-invasive assessment of the coronary artery tree. Computed Tomography - adapted Leaman Score (CT-LeSc) has been shown to be an independent predictor of cardiac events in coronary artery disease (CAD) patients with a score greater than 5 (high).
Purpose:
To investigate the relationship between CT-LeSc and the progression of CAD and to provide vessel- and segment-level CAD qualification and quantification at baseline and 7-year follow-up.
Methods:
Patients with multivessel CAD and CCTA assessments at baseline and follow-up were included. The CT-LeSc analysis was performed in a paired fashion. The patient-level scores and the differences between each phase were assessed by 2 analysts in an independent core laboratory.
Results:
This study analyzed 248 coronary segments from 17 patients with a mean follow-up interval of 7.5 ± 0.6 years. The mean CT-LeSc at baseline and follow-up were 14.6 ± 4.2 and 16.9 ± 1.5, respectively, with an absolute increase of 2.3 ± 1.8. The mean cumulative increase of new lesions was 0.2 ± 0.2 per year. Over time, 14.6% of the non-obstructive lesions became obstructive, and 15.0% of the non-calcified plaques became calcified. There were 29 new lesions found at follow-up, and out of these, 16 were obstructive and 19 were non-calcified.
Conclusion:
In patients at high risk for cardiac events, as determined by CT-LeSc, there was an increase in CT-LeSc, obstructive lesions, and calcified plaques over the 7-year follow-up period. Most of the new lesions were obstructive and non-calcified. This is the first report showing long-term serial imaging CCTA changes in a high-risk population.
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