title: Contemporary model for cardiovascular risk prediction in people with type 2 diabetes.
abstract: 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.
  
  
  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: The George Institute for International Health, The University of Sydney, Australia.
authors: Andre Pascal
authors: AP
authors: Kengne
authors: Anushka
authors: A
authors: Patel
authors: Michel
authors: M
authors: Marre
authors: Florence
authors: F
authors: Travert
authors: Michel
authors: M
authors: Lievre
authors: Sophia
authors: S
authors: Zoungas
authors: John
authors: J
authors: Chalmers
authors: Stephen
authors: S
authors: Colagiuri
authors: Diederick E
authors: DE
authors: Grobbee
authors: Pavel
authors: P
authors: Hamet
authors: Simon
authors: S
authors: Heller
authors: Bruce
authors: B
authors: Neal
authors: Mark
authors: M
authors: Woodward
mesh_terms: D000367:Age Factors; D000368:Aged; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000627:therapeutic use; D001208:Asia / Q000453:epidemiology; D044362:Australasia / Q000453:epidemiology; D001786:Blood Glucose / Q000378:metabolism; D002170:Canada / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000188:drug therapy / Q000453:epidemiology / Q000209:etiology*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000150:complications* / Q000453:epidemiology; D018450:Disease Progression; D004232:Diuretics / Q000627:therapeutic use; D004359:Drug Therapy, Combination; D005060:Europe / Q000453:epidemiology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D007190:Indapamide / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D020913:Perindopril / Q000627:therapeutic use*; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013997:Time Factors
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors; D001786:Blood Glucose; D004232:Diuretics; D007190:Indapamide; D020913:Perindopril