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Predicted 10-year risk of cardiovascular disease is influenced by the risk equation adopted: a cross-sectional analysis
Authors:Benjamin J Gray  Richard M Bracken  Daniel Turner  Kerry Morgan  Stephen D Mellalieu  Michael Thomas  Sally P Williams  Meurig Williams  Sam Rice  Jeffrey W Stephens  on behalf of the Prosiect Sir Gar Group
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’.

Aim

This study examined the differences between four commonly-used CVD risk equations.

Design and setting

Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, south Wales.

Method

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.
Keywords:cardiovascular diseases   decision support techniques   prevention and control   primary care   risk
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