Individualized risk prediction of cardiovascular and kidney outcomes in high risk patients

Abstract

The number of patients with type 2 diabetes, cardiovascular disease and chronic kidney disease is increasing rapidly worldwide. The above diseases are highly interrelated, with shared risk factors. In this thesis, we investigate and identify the relationship between risk factors and the development of cardiovascular and chronic kidney disease in "high-risk" patients with established cardiovascular disease and/or type 2 diabetes. A number of treatments already exist to reduce the risk of cardiovascular and chronic kidney disease, however, these treatments are associated with a risk of side effects, an increase in the number of pills patients must take and, for some therapies, high costs. Therefore, it is important to be able to identify which patients are at increased risk and which would benefit most from preventive medication. Currently, this is done by predicting individual short-term risk of disease. It is important that models for such predictions are accurate and contemporary. In this thesis, we developed DIAL-ESKD, a prediction model developed and validated in approximately 1,000,000 individuals with type 2 diabetes, allowing for 10-year and lifetime predictions of end-stage kidney disease as well as estimated benefit from preventive treatment. Furthermore, we developed SCORE2-diabetes and DIAL2, prediction models for predicting individual 10-year and lifetime risk, respectively, of cardiovascular disease in people with type 2 diabetes and the models were geographically and temporally recalibrated to European regions. These more accurate predictions can be used to improve shared decision making between practitioner and patient in a clinical setting.