Abstract 13573: Prediction of Culprit Lesions in Acute Coronary Syndrome Based Upon Myocardial Mass At-Risk From Coronary CT Angiography

Abstract

Introduction: Stratifying patients with coronary artery disease (CAD) remains challenging. Assessing CAD using myocardial mass at-risk may help identify patients at higher risk of acute coronary syndrome. Hypothesis: This study investigated whether quantitative myocardial mass at-risk (MMAR) can improve prognostication of future culprit coronary lesions at the time of acute coronary syndrome (ACS) events. Methods: From the multicenter, international ICONIC study, 49 patients suspected of coronary artery disease (CAD) going coronary CT angiography imaging (CCTA) were followed to the occurrence of the first ACS. At the time of ACS, culprit lesion was adjudicated by invasive coronary angiography by a group of cardiologists blinded to the previously acquired CCTA. MMAR was calculated for each lesion using the minimum-cost path technique, a previously validated method for calculating myocardial perfusion territories. Left ventricle myocardial segmentation and coronary centerline extraction was performed and used to calculate MMAR distal to all identifiable culprit and non-culprit coronary lesions. Results: In the study sample, 243 coronary lesions were identified on CCTA (194 non-culprit lesions; 49 culprit lesions). The mean MMAR, as a percentage of total left ventricle myocardial mass, for all lesions was 24.6±12.4%. The mean MMAR was 23.1±12.5% and 30.3±10.3% for non-culprit and culprit lesions, respectively (p<0.05). Receiver operating characteristic curve analysis was performed to determine the value of MMAR in predicting a culprit lesion, with an area-under-the-curve analysis for MMAR predicting a culprit lesion of 0.68. Conclusions: MMAR is larger for lesions that will versus will not become culprit at the time of future ACS. The addition of MMAR in the assessment of CAD on CCTA may help stratify the risk of individual coronary lesions.