Computed tomography-based high-risk coronary plaque score to predict acute coronary syndrome among patients with acute chest pain - Results from the ROMICAT II trial

Background: Coronary computed tomography angiography (CTA) can be used to detect and quantitatively assess high-risk plaque features. Objective: To validate the ROMICAT score, which was derived using semi-automated quantitative measurements of high-risk plaque features, for the prediction of ACS. Material and methods: We performed quantitative plaque analysis in 260 patients who presented to the emergency department with suspected ACS in the ROMICAT II trial. The readers used a semi-automated software (QAngio, Medis medical imaging systems BV) to measure high-risk plaque features (volume of <60HU plaque, remodeling index, spotty calcium, plaque length) and diameter stenosis in all plaques. We calculated a ROMICAT score, which was derived from the ROMICAT I study and applied to the ROMICAT II trial. The primary outcome of the study was diagnosis of an ACS during the index hospitalization. Results: Patient characteristics (age 57 +/- 8 vs. 56 +/- 8 years, cardiovascular risk factors) were not different between those with and without ACS (prevalence of ACS 7.8%). There were more men in the ACS group (84% vs. 59%, p = 0.005). When applying the ROMICAT score derived from the ROMICAT I trial to the patient population of the ROMICAT II trial, the ROMICAT score (OR 2.9, 95% Cl 1.4-6.0, p = 0.003) was a predictor of ACS after adjusting for gender and >= 50% stenosis. The AUC of the model containing ROMICAT score, gender, and >= 50% stenosis was 0.91 (95% CI 0.86-0.96) and was better than with a model that included only gender and >= 50% stenosis (AUC 0.85, 95%CI 0.77-0.92; p = 0.002). Conclusions: The ROMICAT score derived from semi-automated quantitative measurements of high-risk plaque features was an independent predictor of ACS during the index hospitalization and was incremental to gender and presence of >50% stenosis. (C) 2015 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.