Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5912
Title: Discrimination and stratification tests of cardiovascular disease risk assessment models against ultrasound detection of carotid plaques in type 2 diabetics
Authors: Chan, LWC 
Sun, Y
Benzie, IFF 
Keywords: Atherosclerosis
Cardiovascular disease
Doppler ultrasound
Risk assessment
Issue Date: Jul-2013
Publisher: Scientific Research
Source: Health, July 2013, v. 5, no. 7A1, p. 1-10 How to cite?
Journal: Health 
Abstract: Atherosclerosis is the early stage of arterial disease, and underlies development of cardio- vascular disease (CVD) and stroke. Although sophisticated models for assessing CVD and stroke risk have been derived based on large- scale prospective studies, their abilities in detect- ing the presence or absence of atherosclerotic plaque have not been investigated. This study aimed to evaluate and compare discriminatory and risk stratifying abilities of 13 CVD risk as- sessment models against the ultrasound detec- tion of carotid plaques in type 2 diabetes melli- tus (T2DM) patients. Forty-nine T2DM subjects were recruited with informed consent, and major anthropomorphic and biomarker data for these models were collected. The model risk scores were evaluated against the carotid plaques de- tected by Doppler ultrasound. Only the FHS- Lpts-CHD-10Y model, which is a variant of the Framingham model, revealed an area under the receiver operating curve (AUROC) that was sig- nificantly different from a random scoring ap- proach (AUROC: 0.681, p < 0.05), and was able to stratify the risk levels of carotid plaque pre- sence (Chi-Square statistic: 5.99, p < 0.05).
URI: http://hdl.handle.net/10397/5912
ISSN: 1949-4998
EISSN: 1949-5005
DOI: 10.4236/health.2013.57A1001
Rights: Copyright © 2013 Lawrence W. C. Chan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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