Assessment of diet quality improves the classification ability of cardiovascular risk score in predicting future events: The 10-year follow-up of the ATTICA study (2002-2012).

Auteur(s) :
Stefanadis C., Panagiotakos DB., Geogousopoulou EN., Pitsavos C.
Date :
Oct, 2014
Source(s) :
EUR J PREV CARDIOL. # p
Adresse :
Department of Dietetics - Nutrition, School of Health Science and Education, Harokopio University, Athens, Greece. [email protected]

Sommaire de l'article

BACKGROUND:

In past years the prediction of cardiovascular disease (CVD) risk has received special attention; however, the presented risk models have so far not been very successful or appreciated.

DESIGN:

The aim of the present work was to examine whether the inclusion of a diet quality evaluation in a CVD risk prediction model is associated with the accuracy of estimating future events.

METHODS:

The working sample consisted of the 2009 ATTICA study participants (aged 18-89 years). The HellenicSCORE (a calibration of the European Society of Cardiology SCORE, based on age, gender, smoking habits, systolic blood pressure and total cholesterol) was calculated as a proxy of heart disease risk, while assessment of diet quality was based on the MedDietScore, which evaluates adherence to a Mediterranean diet. Fatal or non-fatal incidence of CVD (i.e., development of acute coronary syndromes, stroke or other CVD according to WHO-ICD-10 criteria) was calculated using the 10-year follow-up (2002-2012) data of the ATTICA study participants.

RESULTS:

The MedDietScore and the HellenicSCORE were significant predictors of CVD events (p < 0.05). The estimating bias (i.e., misclassification rate of cases) of the model that included only the HellenicSCORE was significantly reduced by the inclusion of MedDietScore in the risk model (Harrell's C = 0.027, p = 0.012), improving the classification ability of the risk model by 56%.

CONCLUSION:

The inclusion of dietary evaluation increased the accuracy of HellenicSCORE risk estimation and, thus, its incorporation into CVD risk prediction scores might help clinicians and public health professionals to better allocate future CVD candidates.

Source : Pubmed
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