Determinants of Non-calcified Low-Attenuation Coronary Plaque Burden in Patients Without Known Coronary Artery Disease: A Coronary CT Angiography Study

Abstract

Background: Although epicardial adipose tissue (EAT) is associated with coronary artery disease (CAD), it is unclear whether EAT volume (EAV) can be used to diagnose high-risk coronary plaque burden associated with coronary events. This study aimed to investigate (1) the prognostic impact of low-attenuation non-calcified coronary plaque (LAP) burden on patient level analysis, and (2) the association of EAV with LAP volume in patients without known CAD undergoing coronary computed tomography angiography (CCTA).

Materials and methods: This retrospective study consisted of 376 patients (male, 57%; mean age, 65.2 ± 13 years) without known CAD undergoing CCTA. Percent LAP volume (%LAP, <30 HU) was calculated as the LAP volume divided by the vessel volume. EAT was defined as adipose tissue with a CT attenuation value ranging from -250 to -30 HU within the pericardial sac. The primary endpoint was a composite event of death, non-fatal myocardial infarction, and unstable angina and worsening symptoms requiring unplanned coronary revascularization >3 months after CCTA. The determinants of %LAP (Q4) were analyzed using a multivariable logistic regression model.

Results: During the follow-up period (mean, 2.2 ± 0.9 years), the primary endpoint was observed in 17 patients (4.5%). The independent predictors of the primary endpoint were %LAP (Q4) (hazard ratio [HR], 3.05; 95% confidence interval [CI], 1.09-8.54; p = 0.033] in the Cox proportional hazard model adjusted by CAD-RADS category. Cox proportional hazard ratio analysis demonstrated that %LAP (Q4) was a predictor of the primary endpoint, independnet of CAD severity, Suita score, EAV, or CACS. The independent determinants of %LAP (Q4) were CACS ≥218.3 (p < 0.0001) and EAV ≥125.3 ml (p < 0.0001). The addition of EAV to CACS significantly improved the area under the curve (AUC) to identify %LAP (Q4) than CACS alone (AUC, EAV + CACS vs. CACS alone: 0.728 vs. 0.637; p = 0.013).

Conclusions: CCTA-based assessment of EAV, CACS, and LAP could help improve personalized cardiac risk management by administering patient-suited therapy.

Keywords: chronic coronary syndrome (CCS); coronary CT angiography; coronary artery calcium score; epicardial adipose tissue; high-risk plaque; prognosis.

Figure 1

Flow chart for the study population. ACS, acute coronary syndrome; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention.

Figure 2

CCTA images and invasive coronary angiography. (A–D) CCTA images of a patient with non-obstructive CAD and increased low-attenuation plaque (Q4) who developed to unstable angina requiring urgent coronary revascularization. The MPR image of the baseline CCTA image for right coronary artery (RCA) showing intermediate stenosis severity with low-attenuation coronary plaque (Q4). (B–D) Cross-sectional images of coronary lesions (white broken bars). (B) Mild stenosis with non-calcified plaque (yellow asterisk). (C) Intermediate stenosis with calcified plaque (white asterisk) and low-attenuation plaque (red asterisk). (D) Intermediate stenosis with calcified and non-calcified plaques. (E) Invasive coronary angiography performed at 12 days following the baseline CCTA examination. Invasive coronary angiography (ICA) image shows intermediate stenosis of the proximal (yellow arrowhead) and distal portions of the RCA (red arrowhead). The patient was managed with conservative strategy, including statins. (F) The patient presented with unstable angina and underwent emergency ICA at 1.5 years following the baseline CCTA examination. The ICA image revealed progression of the coronary lesions (red arrowheads). CCTA, coronary computed tomography angiography; ICA, invasive coronary angiography; MPR, multi-planar reconstruction; RCA, right coronary artery.

Figure 3

Kaplan-Meier curves analysis according to the LAP burden. Kaplan-Meier curve analysis illustrated that patients having %LAP (Q4) had a worse prognosis than those with Q1–Q3 (p < 0.001, log-rank test).

Figure 4

To diagnose patients with %LAP (Q4), the ROC analysis demonstrated that the best cut-off value for CACS was 218.3 Agatston units (A), and that for EAV was 125.3 ml (B). (C) Addition of EAV on CACS significantly improved the AUC that is used to identify %LAP (Q4) than CACS alone (C, EAV + CACS versus CACS alone, 0.728 versus 0.637; p = 0.013).