title: Cardiovascular risk prediction in a population with the metabolic syndrome: Framingham vs. UKPDS algorithms.
abstract: BACKGROUND
  Cardiovascular disease (CVD) risk-prediction algorithms are key in determining one's eligibility for prevention strategies, but are often population-specific. Metabolic syndrome (MetS), a clustering of risk factors that increase the risk of CVD, does not currently have a risk-prediction algorithm available for prediction of CVD. The aim of this study was to compare the predictive capacities of an algorithm intended for 'healthy' individuals and one intended for 'diabetic' individuals.
  
  
  METHODS
  Individual-specific data from 2700 subjects defined as MetS but free of diagnosed CVD from the Australian Diabetes, Obesity and Lifestyle study was used to estimate 5-year risk of CVD using the two algorithms, and compared using Wilcoxon-signed rank test. CVD end point data was used to assess the performance using discrimination and calibration techniques of the two algorithms.
  
  
  RESULTS
  Five-year risk-prediction comparisons demonstrated that the UKPDS algorithm overpredicted risk in the younger age groups (25-54 years) and underpredicted risk in the older age groups (≥55 years) compared to the Framingham algorithm. A total of 133 CVD events occurred over a median follow up of 5.0 years. Model performance analyses demonstrated both the Framingham and UKPDS algorithms were poor at discrimination (area under receiver operator curve 0.513 and 0.524, respectively) and calibration (Hosmer-Lemeshow 467.1 and 297.0, respectively).
  
  
  CONCLUSIONS
  Neither the Framingham or UKPDS algorithms are ideal for prediction of CVD risk in a MetS population. This study highlights the need for development of population-specific risk-prediction algorithms for this growing population group.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, The Alfred Centre, Melbourne, Australia.
authors.forename: Ella
authors.initials: E
authors.lastname: Zomer
authors.forename: Danny
authors.initials: D
authors.lastname: Liew
authors.forename: Alice
authors.initials: A
authors.lastname: Owen
authors.forename: Dianna J
authors.initials: DJ
authors.lastname: Magliano
authors.forename: Zanfina
authors.initials: Z
authors.lastname: Ademi
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Reid
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001315:Australia / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy; D016002:Discriminant Analysis; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy; D008875:Middle Aged; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors