title: Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes.
abstract: OBJECTIVE
  To examine whether index scores based on the EQ-5D, a 5-item generic health status measure, are an independent predictor of vascular events, other major complications and mortality in people with type 2 diabetes and to quantify the relationship between these scores and future survival.
  
  
  METHODS
  Five-year cohort study involving 7348 patients with type 2 diabetes, aged between 50-75 years who had been recruited to the FIELD (Fenofibrate Intervention and Event Lowering in Diabetes) study from Australia and New Zealand.
  
  
  METHODS
  Multivariate Cox proportional hazard regression models were used to estimate the hazard ratio associated with index scores derived from the EQ-5D on: (1) cardiovascular events (including coronary heart disease event, stroke, hospitalization for angina, or cardiovascular death); (2) other major diabetes-related complications (heart failure, amputation, renal dialysis, and lower extremity ulcer); and (3) death from any cause. Life table methods were used to derive expected survival for patients with different index scores.
  
  
  RESULTS
  After adjusting for standard risk factors, a 0.1 higher index score (derived from the UK algorithm) was associated with an additional 7% (95% CI: 4-11%) lower risk of vascular events, a 13% (95% CI: 9-17%) lower risk of complications, and up to 14% (95% CI: 8-19%) lower rate of all-cause mortality.
  
  
  CONCLUSIONS
  Index scores derived from the EQ-5D are an independent predictor of the risk of mortality, future vascular events, and other complications in people with type 2 diabetes. This should be taken into account when extrapolating health outcomes such as quality-adjusted life years (QALYs).
authors.affiliation: School of Public Health, University of Sydney, Sydney, New South Wales. philipc@med.usyd.edu.au
authors.forename: Philip M
authors.initials: PM
authors.lastname: Clarke
authors.forename: Alison J
authors.initials: AJ
authors.lastname: Hayes
authors.forename: Paul G
authors.initials: PG
authors.lastname: Glasziou
authors.forename: Russell
authors.initials: R
authors.lastname: Scott
authors.forename: John
authors.initials: J
authors.lastname: Simes
authors.forename: Anthony C
authors.initials: AC
authors.lastname: Keech
mesh_terms: D000368:Aged; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D002423:Cause of Death; D048909:Diabetes Complications / Q000188:drug therapy / Q000401:mortality*; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000188:drug therapy / Q000401:mortality*; D005260:Female; D011345:Fenofibrate / Q000627:therapeutic use; D006801:Humans; D000960:Hypolipidemic Agents / Q000627:therapeutic use; D015993:Life Tables; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009520:New Zealand / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016016:Proportional Hazards Models; D019057:Quality-Adjusted Life Years; D011795:Surveys and Questionnaires; D016019:Survival Analysis
chemical_list: D000960:Hypolipidemic Agents; D011345:Fenofibrate