|Title||Contemporary model for cardiovascular risk prediction in people with type 2 diabetes|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Kengne, APascal, Patel, A, Marre, M, Travert, F, Lievre, M, Zoungas, S, Chalmers, J, Colagiuri, S, Grobbee, DE, Hamet, P, Heller, SR, Neal, B, Woodward, M|
|Journal||European Journal of Cardiovascular Prevention & Rehabilitation|
Background: Existing cardiovascular risk prediction equations perform non-optimally in different populations with diabetes. Thus, there is a continuing need to develop new equations that will reliably estimate cardiovascular disease (CVD) risk and offer flexibility for adaptation in various settings. This report presents a contemporary model for predicting cardiovascular risk in people with type 2 diabetes mellitus.Design and methods: A 4.5-year follow-up of the Action in Diabetes and Vascular disease: preterax and diamicron-MR controlled evaluation (ADVANCE) cohort was used to estimate coefficients for significant predictors of CVD using Cox models. Similar Cox models were used to fit the 4-year risk of CVD in 7168 participants without previous CVD. The modelâ€™s applicability was tested on the same sample and another dataset.Results: A total of 473 major cardiovascular events were recorded during follow-up. Age at diagnosis, known duration of diabetes, sex, pulse pressure, treated hypertension, atrial fibrillation, retinopathy, HbA1c, urinary albumin/creatinine ratio and non-HDL cholesterol at baseline were significant predictors of cardiovascular events. The model developed using these predictors displayed an acceptable discrimination (c-statistic: 0.70) and good calibration during internal validation. The external applicability of the model was tested on an independent cohort of individuals with type 2 diabetes, where similar discrimination was demonstrated (c-statistic: 0.69).Conclusions: Major cardiovascular events in contemporary populations with type 2 diabetes can be predicted on the basis of routinely measured clinical and biological variables. The model presented here can be used to quantify risk and guide the intensity of treatment in people with diabetes.
Contemporary model for cardiovascular risk prediction in people with type 2 diabetes