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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/34461
Title: Arterial stiffness contributes to coronary artery disease risk prediction beyond the traditional risk score (RAMA-EGAT score)
Authors: Teerapat Yingchoncharoen
Thosaphol Limpijankit
Sutipong Jongjirasiri
Jiraporn Laothamatas
Sukit Yamwong
Piyamitr Sritara
Mahidol University
Keywords: Medicine
Issue Date: 1-Jan-2014
Citation: Heart Asia. Vol.4, No.1 (2014), 77-82
Abstract: Objectives The traditional risk score (RAMA-EGAT) has been shown to be an accurate scoring system for predicting coronary artery disease (CAD). Arterial stiffness measured by the cardioeankle vascular index (CAVI) is known to be a marker of atherosclerotic burden. A study was undertaken to determine whether CAVI improves the prediction of CAD beyond the RAMAEGAT score.Design Cross-sectional study.Patients Patients with a moderate to high risk for CAD by the RAMA-EGAT score were enrolled between November 2005 and March 2006. 64-slice multidetector CT coronary angiography was used to evaluate the coronary artery calcium score and coronary stenosis. Arterial stiffness was assessed by CAVI.Results 1391 patients of median age 59 years (range 31-88) were enrolled in the study, 635 (45.7%) men and 756 (54.3%) women. Of the 1391 patients, 346 (24.87%) had coronary stenosis. There was a correlation between CAVI and the prevalence of coronary stenosis after adjusting for traditional CAD risk factors (OR 3.29). In addition, adding CAVI into the RAMA-EGAT score (modified RAMA-EGAT score) improved the prediction of CAD incidence, increasing C-statistics from 0.72 to 0.85 and resulting in a net reclassification improvement of 27.7% (p<0.0001).Conclusion CAVI is an independent risk predictor for CAD. The addition of CAVI to the RAMA-EGAT score significantly improves the diagnostic yield of CAD.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949117315&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/34461
ISSN: 17591104
Appears in Collections:Scopus 2011-2015

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