Maros Ferencik1, Thomas Mayrhofer2, Stefan B Puchner3, Michael T Lu2, Pal Maurovich-Horvat4, Ting Liu5, Khristine Ghemigian2, Pieter Kitslaar6, Alexander Broersen7, Fabian Bamberg8, Quynh A Truong9, Christopher L Schlett10, Udo Hoffmann2. 1. Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: ferencik@ohsu.edu. 2. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 3. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria. 4. MTA-SE Lendület Cardiovascular Imaging Research Group, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary. 5. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, China. 6. Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands; Medis Medical Imaging Systems B.V, Leiden, The Netherlands. 7. Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands. 8. Department of Radiology, University of Tuebingen, Germany. 9. Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and Weill Cornell Medical College, New York, NY, USA. 10. Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
Abstract
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% CI 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.
RCT Entities:
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, spottycalcium, 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% CI 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.
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