Objective: The purpose of this study is to assess the value of an automated model-based plaque characterization tool for the prediction of major adverse cardiac events (MACE).
Methods: We retrospectively included 45 patients with suspected coronary artery disease of which 16 (33%) experienced MACE within 12 months. Commercially available plaque quantification software was used to automatically extract quantitative plaque morphology: lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core (LRNC) area and matrix area. The measurements were performed at all cross sections, spaced at 0.5 mm, based on fully 3D segmentations of lumen, wall, and each tissue type. Discriminatory power of these markers and traditional risk factors for predicting MACE were assessed.
Results: Regression analysis using clinical risk factors only resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. Based on our plaque morphology analysis, minimal cap thickness, lesion length, LRNC volume, maximal wall area/thickness, the remodeling ratio, and the calcium volume were included into our prognostic model as parameters. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a similar AUC of 0.924.
Conclusion: An automated model based algorithm to evaluate CCTA-derived plaque features and quantify morphological features of atherosclerotic plaque increases the ability for MACE prognostication significantly compared to the use of clinical risk factors alone.
Keywords: Automated analysis; Computed tomography; Coronary artery disease; MACE; Plaque analysis; Prognostication.
Published by Elsevier B.V.