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 model'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.
RCT Entities:
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 model'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.
Authors: Gijs F N Berkelmans; Soffia Gudbjörnsdottir; Frank L J Visseren; Sarah H Wild; Stefan Franzen; John Chalmers; Barry R Davis; Neil R Poulter; Annemieke M Spijkerman; Mark Woodward; Sara L Pressel; Ajay K Gupta; Yvonne T van der Schouw; Ann-Marie Svensson; Yolanda van der Graaf; Stephanie H Read; Bjorn Eliasson; Jannick A N Dorresteijn Journal: Eur Heart J Date: 2019-09-07 Impact factor: 29.983
Authors: Helen C Looker; Marco Colombo; Felix Agakov; Tanja Zeller; Leif Groop; Barbara Thorand; Colin N Palmer; Anders Hamsten; Ulf de Faire; Everson Nogoceke; Shona J Livingstone; Veikko Salomaa; Karin Leander; Nicola Barbarini; Riccardo Bellazzi; Natalie van Zuydam; Paul M McKeigue; Helen M Colhoun Journal: Diabetologia Date: 2015-03-05 Impact factor: 10.122
Authors: Abdul Hakeem Al Rawahi; Patricia Lee; Zaher A M Al Anqoudi; Ahmed Al Busaidi; Muna Al Rabaani; Faisal Al Mahrouqi; Ahmed M Al Busaidi Journal: Oman Med J Date: 2017-03
Authors: José A Piniés; Fernando González-Carril; José M Arteagoitia; Itziar Irigoien; Jone M Altzibar; José L Rodriguez-Murua; Larraitz Echevarriarteun Journal: Diabetologia Date: 2014-09-12 Impact factor: 10.122