Cardiovascular disease risk score prediction models for women and its applicability to Asians
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2014Type
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This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/ Please refer to the licence to obtain terms for any further reuse or distribution of this work.
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Purpose: Although elevated cardiovascular disease (CVD) risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Methods: Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation) risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20–69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Results: Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were consistent with Australian CVD prevalence. Conclusion: In summary, ethnicity needs to be incorporated into CVD risk assessment. Australian standards used to quantify risk and treat women could be applied to Asians in the interim. The SCORE risk chart for low-risk regions and Framingham risk score model for incidence are recommended. The inclusion of other relevant risk variables such as obesity, poor diet/nutrition, and low levels of physical activity may improve risk estimation.
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