Cardiovascular disease risk assessment in older women: can we improve on Framingham? British Women's Heart and Health prospective cohort study.
May, M;
Lawlor, DA;
Brindle, P;
Patel, R;
Ebrahim, S;
(2006)
Cardiovascular disease risk assessment in older women: can we improve on Framingham? British Women's Heart and Health prospective cohort study.
Heart (British Cardiac Society), 92 (10).
pp. 1396-1401.
ISSN 1355-6037
DOI: https://doi.org/10.1136/hrt.2005.085381
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OBJECTIVES: To develop a cardiovascular risk assessment tool that is feasible and easy to use in primary care (general practice (GP) model). DESIGN: Prospective cohort study. SETTING: 23 towns in the United Kingdom. PARTICIPANTS: 3582 women aged 60 to 79 years who were free of coronary heart disease (CHD) at entry into the British Women's Heart and Health Study. MAIN OUTCOME MEASURES: Predictive performance of a GP model compared with the standard Framingham model for both CHD and cardiovascular disease (CVD). RESULTS: The Framingham tool predicted CHD events over 5 years accurately (predicted 5.7%, observed 5.5%) but overpredicted CVD events (predicted 10.5%, observed 6.8%). In higher-risk groups, Framingham overpredicted both CHD and CVD events and was poorly calibrated for this cohort. Including C-reactive protein and fibrinogen with standard Framingham risk factors did not improve discrimination of the model. The GP model, which used age, systolic blood pressure, smoking habit and self-rated health (all of which can be easily obtained in one surgery visit) performed as well as the Framingham risk tool: area under the receiver operating curve discrimination statistic was 0.66 (95% confidence interval (CI) 0.62 to 0.70) for CHD and 0.67 (95% CI 0.64 to 0.71) for CVD compared with 0.65 (95% CI 0.61 to 0.68) and 0.66 (95% CI 0.62 to 0.69) for the corresponding Framingham models. CONCLUSIONS: An alternative risk assessment based on only a simple routine examination and a small number of pertinent questions may be more useful in the primary care setting. This model appears to perform well but needs to be tested in different populations.