The EPIC-Norfolk Study
A simple risk score using routine data for predicting cardiovascular disease in primary care.
The British journal of general practice : the journal of the Royal College of General Practitioners 2010 ; 60: e327-34.
Chamnan P, Simmons RK, Hori H, Sharp SJ, Khaw KT, Wareham NJ, and Griffin SJ
DOI : 10.3399/bjgp10X515098
PubMed ID : 20822683
PMCID : 0
URL : https://pubmed.ncbi.nlm.nih.gov/20822683/
Abstract
Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.
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.
Prospective cohort study (EPIC-Norfolk).
Norfolk area, UK.
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).
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%.
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.