|Title||Cardiovascular risk prediction in the Netherlands|
|Author(s)||Dis, S.J. van|
|Source||University. Promotor(en): Daan Kromhout, co-promotor(en): W.M.M. Verschuren; Marianne Geleijnse. - [S.l.] : S.n. - ISBN 9789461730893|
Chair Nutrition and Disease
|Publication type||Dissertation, internally prepared|
|Keyword(s)||hart- en vaatziekten - risicoschatting - obesitas - afstamming - genetische effecten - nederland - cardiovascular diseases - risk assessment - obesity - parentage - genetic effects - netherlands|
|Categories||Public Health / Epidemiology|
Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These functions use classical risk factors (age, gender, smoking, blood pressure and the ratio of total-to-HDL-cholesterol) to predict absolute 10-year risk of CVD mortality rather than total (fatal plus nonfatal) CVD. The aim of this thesis was to improve cardiovascular risk prediction in the Netherlands and to correctly classify high-risk persons.
Methods: We primarily used data from the Monitoring Project on Chronic Disease Risk Factors (MORGEN project) of the National Institute for Public Health and the Environment (RIVM). Risk factor data of more than 20,000 men and women aged 20-65 years were collected between 1993 and 1997. Ten-year follow up data on CVD mortality and morbidity were obtained from Statistics Netherlands and the National Hospital Discharge Register, respectively. Risk functions were developed using multivariable Cox proportional hazard models.
Results: The SCORE risk function for low-risk countries was the best predictor of CVD mortality in the Netherlands. Total CVD was approximately four times higher than CVD mortality. Obesity (BMI ≥30 kg/m2) and parental history of myocardial infarction before age 70 were independent predictors of total CVD. Risk functions predicting risk of CVD mortality and total CVD, and their ability to discriminate between future cases and non-cases, did not differ. Of the high-risk persons with a CVD mortality risk of at least 5%, approximately 20% developed a nonfatal or fatal CVD event during 10 years of follow-up. When a cut-off point of 2% CVD mortality was used, approximately 10% of the high-risk persons developed a CVD event. When obesity and parental history of MI were added to the classical risk factor function, correct risk classification improved by 5%. This improvement in risk prediction was mainly due to obesity.
Conclusions: Discrimination between future cases and non-cases did not improve by expanding the endpoint of risk prediction from fatal CVD to total CVD. Adding obesity and parental history to the classical risk factor functions slightly increased the number of correctly classified persons.