A simple risk score using routine data for predicting cardiovascular disease in primary care

  • Chamnan P
  • Simmons R
  • Hori H
 et al. 
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Abstract

Background
Population-based screening for cardiovascular disease (CVD) risk,
incorporating blood tests, is proposed in several countries.
Aim
The aim of this study was to evaluate whether a simple approach to
identifying individuals at high risk of CVD using routine data might be
effective.
Design of study
Prospective cohort study (EPIC-Norfolk).
Setting
Norfolk area, UK. Method A total of 21 867 men and women aged 40-74
years, who were free from CVD and diabetes at baseline, participated in
the study. The discrimination (the area under the receiver operating
characteristic curve {{}[{}}aROC]), calibration, sensitivity/specificity,
and positive/negative predictive value were evaluated for different risk
thresholds of the Framingham risk equations and the Cambridge diabetes
risk score (as an example of a simple risk score using routine data from
electronic general practice records).
Results
During 203 664 person-years of follow-up, 2213 participants developed a
first CVD event (10.9 per 1000 person-years). The Cambridge diabetes
risk score predicted CVD events reasonably well (aROC 0.72; 95{%}
confidence interval {{}[{}}CI] = 0.71 to 0.73), while the Framingham risk
score had the best predictive ability (aROC 0.77; 95{%} CI = 0.76 to
0.78). The Framingham risk score overestimated risk of developing CVD in
this representative British population by 60{%}.
Conclusion
A risk score incorporating routinely available data from GP records
performed reasonably well at predicting CVD events. This suggests that
it might be more efficient to use routine data as the first stage in a
stepwise population screening programme to identify:people at high risk
of developing CVD before more time- and resource-consuming tests are
used.

Author-supplied keywords

  • Cardiovascular disease
  • Diabetes
  • Prediction
  • Primary care
  • Risk assessment

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Authors

  • Parinya Chamnan

  • Rebecca K. Simmons

  • Hiroyuki Hori

  • Stephen Sharp

  • Kay Tee Khaw

  • Nicholas J. Wareham

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