FROM PREDICTED RISK TO PREDICTED BURDEN OF CARDIOVASCULAR DISEASE
Method(s): Data from a large Dutch cohort study (n=19484; mean follow up 12.3 years) was used to investigate differences in composite endpoints of four widely used CVD risk prediction models: the Adult Treatment Panel III (ATP), Framingham Global (FRS), Pooled Cohort Equations (PCE) and SCORE-low (SCORE) models. Across these four prediction models, we calculated the 10-year individual CVD risks and the corresponding health loss based on the CVD event types included in the composite endpoint. Subsequently, each prediction model was used to estimate the expected CVD burden in the 25% individuals with highest predicted risks, expressed as Quality-Adjusted Life Years (QALYs) lost.
Result(s): The observed constitution of the composite endpoints varied widely across the four models. For example, the percentage non-fatal MI events was 81%, 19%, 37%, and 0% according to ATP, FRS, PCE, and SCORE respectively, and for fatal MI this was 19%, 5%, 9%, and 57%, respectively. FRS predicted the highest CVD risks and the composite endpoint used in SCORE had the highest health burden. The predicted CVD burden in the 25% individuals with highest predicted risk was 0.19, 0.72, 0.36, and 0.23 QALYs lost per individual when using ATP, FRS, PCE and SCORE, respectively.
Conclusion(s): The investigated CVD risk prediction models showed huge variation in definition and constitution of the composite endpoints. This directly resulted in large differences in estimated CVD burden. When interpreting the estimated CVD burden derived with a risk prediction model, it is therefore crucial to consider which CVD event types are included, and which are excluded, in that prediction model.