title: New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort.
abstract: BACKGROUND
  New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data.
  
  
  OBJECTIVE
  To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme.
  
  
  METHODS
  People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications.
  
  
  RESULTS
  Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model.
  
  
  CONCLUSIONS
  The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided.
authors.affiliation: Department of General Practice and Primary Health Care, The University of Auckland, PB 92019, Auckland, New Zealand. t.robinson@auckland.ac.nz
authors.forename: Tom
authors.initials: T
authors.lastname: Robinson
authors.forename: C Raina
authors.initials: CR
authors.lastname: Elley
authors.forename: Sue
authors.initials: S
authors.lastname: Wells
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Robinson
authors.forename: Tim
authors.initials: T
authors.lastname: Kenealy
authors.forename: Romana
authors.initials: R
authors.lastname: Pylypchuk
authors.forename: Dale
authors.initials: D
authors.lastname: Bramley
authors.forename: Bruce
authors.initials: B
authors.lastname: Arroll
authors.forename: Sue
authors.initials: S
authors.lastname: Crengle
authors.forename: Tania
authors.initials: T
authors.lastname: Riddell
authors.forename: Shanthi
authors.initials: S
authors.lastname: Ameratunga
authors.forename: Patricia
authors.initials: P
authors.lastname: Metcalf
authors.forename: Paul L
authors.initials: PL
authors.lastname: Drury
mesh_terms: D017668:Age of Onset; D000419:Albuminuria / Q000652:urine; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000209:etiology* / Q000517:prevention & control; D015331:Cohort Studies; D003404:Creatinine / Q000652:urine; D020000:Decision Support Systems, Clinical; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000453:epidemiology; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D009520:New Zealand / Q000453:epidemiology; D012372:ROC Curve; D012307:Risk Factors
chemical_list: D003404:Creatinine; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human