Browsing by Author "Smeeth Liam...et al"

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    Cardiovascular disease risk prediction in sub-Saharan African populations — Comparative analysis of risk algorithms in the RODAM study
    (ELSEVIER, 2017) Boateng Daniel; Agyemang Charles; Beune Erik; Owusu-Dabo Ellis; Meeks Karlijn; Smeeth Liam...et al
    Background: Validated absolute risk equations are currently recommended as the basis of cardiovascular disease (CVD) risk stratification in prevention and control strategies. However, there is no consensus on appropriate equations for sub-Saharan African populations. We assessed agreement between different cardiovascular risk equations among Ghanaian migrant and home populations with no overt CVD. Methods: The 10-year CVD risks were calculated for 3586 participants aged 40–70 years in the multi-centre RODAM study among Ghanaians residing in Ghana and Europe using the Framingham laboratory and non laboratory and Pooled Cohort Equations (PCE) algorithms. Participants were classified as low, moderate or high risk, corresponding to b10%, 10–20% and N20% respectively. Agreement between the risk algorithms was assessed using kappa and correlation coefficients. Results: 19.4%, 12.3% and 5.8% were ranked as high 10-year CVD risk by Framingham non-laboratory, Framing ham laboratory and PCE, respectively. The median (25th–75th percentiles) estimated 10-year CVD risk was9.5% (5.4–15.7), 7.3% (3.9–13.2) and 5.0% (2.3–9.7) for Framingham non-laboratory, Framingham laboratory and PCE, respectively. The concordance between PCE and Framingham non-laboratory was better in the home Ghanaian population (kappa = 0.42, r = 0.738) than the migrant population (kappa = 0.24, r = 0.732) whereas concordance between PCE and Framingham laboratory was better in migrant Ghanaians (kappa = 0.54, r = 0.769) than the home population (kappa = 0.51, r = 0.758). Conclusion: CVD prediction with the same algorithm differs for the migrant and home populations and the inter changeability of Framingham laboratory and non-laboratory algorithms is limited. Validation against CVD outcomes is needed to inform appropriate selection of risk algorithms for use in African ancestry populations