Abstract
BACKGROUND: Total atherosclerotic plaque burden assessment by CT angiography (CTA) is a promising tool for diagnosis and prognosis of coronary artery disease (CAD) but its validation is restricted to small clinical studies. We tested the feasibility of semi-automatically derived coronary atheroma burden assessment for identifying patients with hemodynamically significant CAD in a large cohort of patients with heterogenous characteristics. METHODS: This study focused on the CTA component of the CORE320 study population. A semi-automated contour detection algorithm quantified total coronary atheroma volume defined as the difference between vessel and lumen volume. Percent atheroma volume (PAV = [total atheroma volume/total vessel volume] × 100) was the primary metric for assessment (n = 374). The area under the receiver operating characteristic curve (AUC) determined the diagnostic accuracy for identifying patients with hemodynamically significant CAD defined as ≥50% stenosis by quantitative coronary angiography and associated myocardial perfusion abnormality by SPECT. RESULTS: Of 374 patients, 139 (37%) had hemodynamically significant CAD. The AUC for PAV was 0.78 (95% confidence interval [CI] 0.73-0.83) compared with 0.84 [0.79-0.88] by standard expert CTA interpretation (p = 0.02). Accuracy for both CTA (0.91 [0.87, 0.96]) and PAV (0.86 [0.81-0.91]) increased after excluding patients with history of CAD (p < 0.01 for both). Bland-Altman analysis revealed good agreement between two observers (bias of 280.2 mm(3) [161.8, 398.7]). CONCLUSIONS: A semi-automatically derived index of total coronary atheroma volume yields good accuracy for identifying patients with hemodynamically significant CAD, though marginally inferior to CTA expert reading. These results convey promise for rapid, reliable evaluation of clinically relevant CAD.