title: A simple risk score using routine data for predicting cardiovascular disease in primary care.
abstract: BACKGROUND
  Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  Prospective cohort study (EPIC-Norfolk).
  
  
  METHODS
  Norfolk area, UK.
  
  
  METHODS
  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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge.
authors.forename: Parinya
authors.initials: P
authors.lastname: Chamnan
authors.forename: Rebecca K
authors.initials: RK
authors.lastname: Simmons
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Hori
authors.forename: Stephen
authors.initials: S
authors.lastname: Sharp
authors.forename: Kay-Tee
authors.initials: KT
authors.lastname: Khaw
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Wareham
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Griffin
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000209:etiology*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors
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