Individualized cardiovascular disease prevention: risk factors, risk prediction, and clinical implementation

Abstract

Cholesterol and blood pressure-lowering, and antithrombotic medications are important tools used by doctors to prevent cardiovascular disease (CVD). Ideally, the benefits outweigh the costs and harms of therapy. However, this balance can differ between individuals this balance can be different, and finding where this balance lays can be challenging. The benefit an individual can expect from medication depends on a complex interplay of multiple factors, such as risk of a cardiovascular event, risk of a ‘competing’ event such as death from cancer, the therapy being prescribed, and the levels of risk factors such as blood pressure and cholesterol. The effect of CVD-prevention strategies for an individual can serve as a central communication point in the shared-decision making process. In this thesis, we investigated "traditional" risk-factors, developed models to estimate lifelong therapy-effects, and investigated the clinical implications of using the models and estimates. We found that even though body mass index adds little to short-term prediction models, people with a high amount of visceral fat tissue (the fat around the organs) were still at an increased risk of death. Also, risk prediction models did not improve when response to blood-pressure therapy was added as a predictor. We further developed the LIFE-CVD model, which provides personalized estimated for the (lifelong) effect of CVD-prevention strategies. This model has been incorporated into an online tool (www.U-Prevent.com). We also found that people generally desire a greater gain in healthy life-expectancy from preventive therapy than clinically feasible, but that providing people with personalized estimates did not discourage them from taking their medications.