title: Predicted 10-year risk of cardiovascular disease is influenced by the risk equation adopted: a cross-sectional analysis.
abstract: BACKGROUND
  Validated risk equations are currently recommended to assess individuals to determine those at 'high risk' of cardiovascular disease (CVD). However, there is no longer a risk 'equation of choice'.
  
  
  OBJECTIVE
  This study examined the differences between four commonly-used CVD risk equations.
  
  
  METHODS
  Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, south Wales.
  
  
  METHODS
  Analysis of 790 individuals (474 females, 316 males) with no prior diagnosis of CVD or diabetes. Ten-year CVD risk was predicted by entering the relevant variables into the QRISK2, Framingham Lipids, Framingham BMI, and JBS2 risk equations.
  
  
  RESULTS
  The Framingham BMI and JBS2 risk equations predicted a higher absolute risk than the QRISK2 and Framingham Lipids equations, and CVD risk increased concomitantly with age irrespective of which risk equation was adopted. Only a small proportion of females (0-2.1%) were predicted to be at high risk of developing CVD using any of the risk algorithms. The proportion of males predicted at high risk ranged from 5.4% (QRISK2) to 20.3% (JBS2). After age stratification, few differences between isolated risk factors were observed in males, although a greater proportion of males aged ≥50 years were predicted to be at 'high risk' independent of risk equation used.
  
  
  CONCLUSIONS
  Different risk equations can influence the predicted 10-year CVD risk of individuals. More males were predicted at 'high risk' using the JBS2 or Framingham BMI equations. Consideration should also be given to the number of isolated risk factors, especially in younger adults when evaluating CVD risk.
authors.affiliation: Diabetes Research Group, College of Medicine; Applied Sports Technology Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Gray
authors.affiliation: Diabetes Research Group, College of Medicine; Applied Sports Technology Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Bracken
authors.affiliation: Diabetes Research Group, College of Medicine; Applied Sports Technology Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea.
authors.forename: Daniel
authors.initials: D
authors.lastname: Turner
authors.affiliation: Hywel Dda Health Board, Prince Philip Hospital, Llanelli.
authors.forename: Kerry
authors.initials: K
authors.lastname: Morgan
authors.affiliation: Applied Sports Technology Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea.
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Mellalieu
authors.affiliation: Public Health Wales, Carmarthen.
authors.forename: Michael
authors.initials: M
authors.lastname: Thomas
authors.affiliation: TATA Steel Packaging Recycling, Trostre, Llanelli.
authors.forename: Sally P
authors.initials: SP
authors.lastname: Williams
authors.affiliation: Hywel Dda Health Board, Prince Philip Hospital, Llanelli.
authors.forename: Meurig
authors.initials: M
authors.lastname: Williams
authors.affiliation: Hywel Dda Health Board, Prince Philip Hospital, Llanelli.
authors.forename: Sam
authors.initials: S
authors.lastname: Rice
authors.affiliation: Diabetes Research Group, College of Medicine, Swansea University, Swansea.
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Stephens
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D001794:Blood Pressure; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D002784:Cholesterol / Q000378:metabolism*; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D017678:Sex Distribution; D011795:Surveys and Questionnaires; D014852:Wales / Q000453:epidemiology
chemical_list: D001786:Blood Glucose; D002784:Cholesterol
keywords: cardiovascular diseases; decision support techniques; prevention and control; primary care; risk