Impact of preventive screening and lifestyle interventions in women with a history of preeclampsia: A micro-simulation study.

BACKGROUND: Preeclampsia is a female-specific risk factor for the development of future cardiovascular disease. Whether early preventive cardiovascular disease risk screenings combined with risk-based lifestyle interventions in women with previous preeclampsia are beneficial and cost-effective is unknown. METHODS: A micro-simulation model was developed to assess the life-long impact of preventive cardiovascular screening strategies initiated after women experienced preeclampsia during pregnancy. Screening was started at the age of 30 or 40 years and repeated every five years. Data (initial and follow-up) from women with a history of preeclampsia was used to calculate 10-year cardiovascular disease risk estimates according to Framingham Risk Score. An absolute risk threshold of 2% was evaluated for treatment selection, i.e. lifestyle interventions (e.g. increasing physical activity). Screening benefits were assessed in terms of costs and quality-adjusted-life-years, and incremental cost-effectiveness ratios compared with no screening. RESULTS: Expected health outcomes for no screening are 27.35 quality-adjusted-life-years and increase to 27.43 quality-adjusted-life-years (screening at 30 years with 2% threshold). The expected costs for no screening are €9426 and around €13,881 for screening at 30 years (for a 2% threshold). Preventive screening at 40 years with a 2% threshold has the most favourable incremental cost-effectiveness ratio, i.e. €34,996/quality-adjusted-life-year, compared with other screening scenarios and no screening. CONCLUSIONS: Early cardiovascular disease risk screening followed by risk-based lifestyle interventions may lead to small long-term health benefits in women with a history of preeclampsia. However, the cost-effectiveness of a lifelong cardiovascular prevention programme starting early after preeclampsia with risk-based lifestyle advice alone is relatively unfavourable. A combination of risk-based lifestyle advice plus medical therapy may be more beneficial.

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

BACKGROUND: Cardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice. METHOD: The exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models. RESULTS: Due to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively. CONCLUSION: Widely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD.