Individualized cardiovascular disease prevention: Clinical implementation of risk prediction

Abstract

Cardiovascular diseases are the most common non-communicable diseases globally. In the prevention of cardiovascular events, effective strategies have been developed by reduction of the most important modifiable risk factors, like systolic blood pressure and cholesterol. To most effectively target such preventive measures, individuals who benefit most are often identified using prediction models that predict an individual’s cardiovascular event risk. In this thesis, we improve upon such predictions of cardiovascular event risk by updating existing models, or by the development of new models. One of the models which is renewed, is the SCORE2 model for the prediction of 10-year risk of cardiovascular disease for people without previous cardiovascular disease and without diabetes. Important improvements include better representation of geographical differences in disease incidence, and the use of large, contemporary datasets. Previous guidelines only recommended such prediction models for healthy middle-aged people without cardiovascular disease and diabetes. In the current thesis, models were also developed or improved for other populations, such as the elderly and people with previous vascular diseases, which is now also included in the 2021 European prevention guidelines. Furthermore, the effectiveness of treatment strategies based on predicted treatment benefit has been evaluated, showing that these are effective in the prevention of vascular disease. These methodological and clinical improvements of cardiovascular risk prediction may facilitate informed individual treatment decisions. All models discussed in this thesis are available, or will become available online at www.U-Prevent.com.