ASSESSING CVD RISK AND BURDEN: DIFFERENCES IN PREDICTION MODELS AND POPULATIONS

Purpose: Prediction models for cardiovascular disease (CVD) are important to assess the burden of disease. However, it is likely that the definition of the composite endpoints (CEP) influences CVD burden estimates, and that the distribution of event types into the CEP is age and gender dependent. This complicates robust assessment of the potential (preventable) CVD burden. Therefore, the purpose of this paper is to identify how differences in the type and severity of different CVD events, and differences in commonly used CVD risk prediction models, influence CVD burden estimates across different populations.

Method(s): Data from 20.423 participants of the MORGEN cohort was used and classified into subgroups based on age and gender. CVD events were identified for four prediction models: ATP, Framingham, PCE and SCORE. The 10-year CVD risks and associated burdens, expressed as Quality-Adjusted Life Years (QALYs) lost, were determined and presented for high-risk individuals, i.e. the 25% individuals with highest predicted risks. The effect of a hypothetical (risk factor) treatment in high-risk individuals was investigated, regarding an overall and event specific risk reduction.

Result(s): The distribution of CVD event types varied between men and women but not with age. The predicted risks, as expected, differed substantially with gender and age. Consequently, the predicted burden varied between men and women, and between age-groups, mainly due to differences in predicted risks. For high-risk individuals, men each lost 0.22, 0.83, 0.18, and 0.33 QALYs according to ATP, Framingham, PCE, and SCORE, and women lost 0.07, 0.50, 0.17, 0.13 QALYs, respectively. When treating these high-risk individuals, the burden for men decreased to 0.14, 0.54, 0.12, and 0.22 QALYs lost, and for women to 0.05, 0.33, 0.11, and 0.09 QALYs lost, according to ATP, Framingham, PCE, and SCORE, respectively.

Conclusion(s): Estimates of CVD burden depend as much on the CVD event types included in risk prediction models as on the risk estimates produced by such models. Investigating the distribution of CVD events occurring in practice is therefore necessary to obtain robust estimates of CVD burden and the potential reduction from preventive strategies. Furthermore, as the risks and consequences of specific CVD events are demonstrated to differ for gender and age, evidence of the distribution of CVD events should be obtained for the considered population targeted for preventive strategies.