title: Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart.
abstract: BACKGROUND
  Development of a validated risk prediction model for future cardiovascular disease (CVD) in Australians is a high priority for cardiovascular health strategies.
  
  
  METHODS
  Recalibration of the SCORE (Systematic COronary Risk Evaluation) risk chart based on Australian national mortality data and average major CVD risk factor levels.
  
  
  METHODS
  Australian national mortality data (2003-2005) were used to estimate 10-year cumulative CVD mortality rates for people aged 40-74 years. Average age-specific and sex-specific levels of systolic blood pressure, total cholesterol and prevalence of current smoking were generated from data obtained in eight Australian large-scale population-based surveys undertaken from the late 1980s. The SCORE risk chart was then recalibrated by applying hazard ratios for 10-year CVD mortality obtained in the SCORE project. Discrimination and calibration of the recalibrated model was evaluated and compared with that of the original SCORE and Framingham equations in the Blue Mountains Eye Study in Australia using Harrell's c and Hosmer-Lemeshow chi statistics, respectively.
  
  
  RESULTS
  An Australian risk prediction chart for CVD mortality was derived. Among 1998 Blue Mountains Eye Study participants aged 49-74 years with neither CVD nor diabetes at baseline, the Harrell's c statistics for the Australian risk prediction chart for CVD mortality were 0.76 (95% confidence interval: 0.69-0.84) and 0.71 (confidence interval: 0.62-0.80) in men and women, respectively. The corresponding Hosmer-Lemeshow chi statistics, the measure of calibration, were 2.32 (P = 0.68) and 7.43 (P = 0.11), which were superior to both the SCORE and Framingham equations.
  
  
  CONCLUSIONS
  This new tool provides a valid and reliable method to predict risk of CVD mortality in the general Australian population.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Tonkin
authors.forename: Lynelle
authors.initials: L
authors.lastname: Moon
authors.forename: Paul
authors.initials: P
authors.lastname: Mitchell
authors.forename: Annette
authors.initials: A
authors.lastname: Dobson
authors.forename: Graham
authors.initials: G
authors.lastname: Giles
authors.forename: Michael
authors.initials: M
authors.lastname: Hobbs
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Phillips
authors.forename: Jonathan E
authors.initials: JE
authors.lastname: Shaw
authors.forename: David
authors.initials: D
authors.lastname: Simmons
authors.forename: Leon A
authors.initials: LA
authors.lastname: Simons
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Fitzgerald
authors.forename: Guy
authors.initials: G
authors.lastname: De Backer
authors.forename: Dirk
authors.initials: D
authors.lastname: De Bacquer
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D002138:Calibration; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000401:mortality*; D005260:Female; D006305:Health Status Indicators; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors