To determine whether the assessment of individual plaques is superior in predicting the progression to obstructive coronary artery disease (CAD) on serial coronary computed tomography angiography (CCTA) than per-patient assessment. From a multinational registry of 2252 patients who underwent serial CCTA at a ≥ 2-year inter-scan interval, patients with only non-obstructive lesions at baseline were enrolled. CCTA was quantitatively analyzed at both the per-patient and per-lesion level. Models predicting the development of an obstructive lesion at follow up using either the per-patient or per-lesion level CCTA measures were constructed and compared. From 1297 patients (mean age 60 ± 9 years, 43% men) enrolled, a total of 3218 non-obstructive lesions were identified at baseline. At follow-up (inter-scan interval: 3.8 ± 1.6 years), 76 lesions (2.4%, 60 patients) became obstructive, defined as > 50% diameter stenosis. The C-statistics of Model 1, adjusted only by clinical risk factors, was 0.684. The addition of per-patient level total plaque volume (PV) and the presence of high-risk plaque (HRP) features to Model 1 improved the C-statistics to 0.825 [95% confidence interval (CI) 0.823–0.827]. When per-lesion level PV and the presence of HRP were added to Model 1, the predictive value of the model improved the C-statistics to 0.895 [95% CI 0.893–0.897]. The model utilizing per-lesion level CCTA measures was superior to the model utilizing per-patient level CCTA measures in predicting the development of an obstructive lesion (p < 0.001). Lesion-level analysis of coronary atherosclerotic plaques with CCTA yielded better predictive power for the development of obstructive CAD than the simple quantification of total coronary atherosclerotic burden at a per-patient level. Clinical Trial Registration: ClinicalTrials.gov
|Number of pages||8|
|Journal||International Journal of Cardiovascular Imaging|
|Publication status||Published - 2020 Dec|
Bibliographical noteFunding Information:
This work was supported by the Leading Foreign Research Institute Recruitment Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (MSIT) (Grant No. 2012027176) and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2018-0-00861, Intelligent SW Technology Development for Medical Data Analysis). The study was also funded in part by a generous gift from the Dalio Institute of Cardiovascular Imaging and the Michael Wolk Foundation. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit the manuscript for publication.
Dr. Chang receives funding from the Leading Foreign Research Institute Recruitment Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (MSIT) (Grant No. 2012027176). Dr. James K. Min receives funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare, and has an equity interest in Cleerly. Dr. Habib Samady serves on the scientific advisory board of Philips, has equity interest in Covanos Inc., and has a research grant from Medtronic. The remaining authors have no relevant disclosures.
© 2020, Springer Nature B.V.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Cardiology and Cardiovascular Medicine