Carotid Intima Media Thickness Versus Carotid Plaque in Cardiovascular Risk Evaluation
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Carotid Intima Media Thickness Versus Carotid Plaque in Cardiovascular Risk Evaluation Smita Negi & Tyler Murray & Vijay Nambi
Published online: 19 January 2011 # Springer Science+Business Media, LLC 2011
Abstract Cardiovascular (CV) risk (ie, coronary heart disease [CHD] risk + stroke risk) prediction can be improved with the use of imaging modalities. Carotid intima media thickness (CIMT) and carotid plaque have been used as markers of atherosclerosis and also as predictors of incident CV events. It has been shown that adding either or both to traditional risk prediction models improves CHD risk prediction. There are limited data comparing CIMT and carotid plaque and currently it appears that combining both CIMT and carotid plaque best improves CHD risk prediction. Improvements in ultrasound technology may allow for improved assessment of CIMT, and quantifying and possibly characterizing atherosclerotic plaque in the near future. Whether quantifying plaque will be superior to CIMT measurements or if the combination of both will be the best way to improve CVD (CHD + stroke) risk prediction with ultrasound remains to be seen. Keywords Carotid intima media thickness . Carotid plaque . Atherosclerosis . Cardiovascular risk stratification
S. Negi : T. Murray : V. Nambi (*) Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, 6565 Fannin Street, M.S. A-601, Houston, TX 77030, USA e-mail: [email protected] S. Negi : T. Murray : V. Nambi Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart and Vascular Center, Houston, TX, USA
Introduction Cardiovascular (CV) risk estimation is essential in both identifying an at-risk population and in administering preventive therapies. Population-based risk prediction algorithms such as Framingham Risk Score (FRS) [1], Atherosclerosis Risk in Community (ARIC) Risk Score [2], and the SCORE [3] are commonly used to assess coronary heart disease (CHD) risk. Although, these risk scores using traditional risk factors (TRF) are reasonably effective in risk prediction, they do have some inherent limitations. First, they classify about 75 % of the population at risk into the low- or intermediate-risk categories. Further, about 60% of the CHD events occur in these risk groups [4]. TRF-based risk estimation provides significant weight to the age of an individual and thus may not accurately predict risk in a younger population [5]. Most TRF-based risk prediction models assess only a 10-year or short-term risk, and do not address the lifetime risk of an individual, thereby sometimes giving a false sense of security. Additionally, most of these algorithms were only designed to assess CHD event risk. Stroke is as important a CV event as heart attack, and although there are stroke risk prediction tools such as the Framingham Stroke Score [6] and the ARIC [7] stroke risk score, these are seldom used. Strategies that assess risk for both CHD and stroke may be better suited for global CV risk assessment. However, whenever end points ar
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