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Comparison of 3 risk estimators to guide initiation of statin therapy for primary prevention of cardiovascular disease

Comparison of 3 risk estimators to guide initiation of statin therapy for primary prevention of cardiovascular disease

Journal of Clinical Lipidology 11(6): 1441-1447

Among high-risk individuals, statins are beneficial for primary prevention of cardiovascular disease (CVD). In Nigeria, currently, there are no CVD prevention guidelines, so the use of CVD risk estimation to guide statin therapy is left to the discretion of the physician. The objective of the study was to compare 3 CVD risk estimation tools in the evaluation of patients presenting to a tertiary hospital in Nigeria. Cross-sectional study involving 295 patients with any CVD risk factors but not taking statins. Traditional CVD risk factors were assessed with a standard questionnaire and laboratory evaluation. Ten-year CVD risk was estimated with American College of Cardiology/American Heart Association Atherosclerotic Cardiovascular Disease (ACC/AHA ASCVD) Risk Estimator (2013), Framingham Risk Score (Framingham Risk Score [FRS] 2008), and the World Health Organisation/International Society of Hypertension (WHO/ISH) risk prediction chart for Africa Region D. Kappa statistic was used to determine agreement among the estimators. The mean age was 48.4 ± 10.4 years; 60.7% were females. Risk factors for CVD were hypertension (56.3%), dyslipidemia (41.4%), diabetes (20%), obesity (28.5%), and cigarette smoking (4.4%). In all, 50.2%, 16.9%, and 15.2% were classified as high risk using the ACC/AHA ASCVD Risk Estimator, FRS 2008, and WHO/ISH risk chart, respectively. The agreement was moderate between FRS and WHO/ISH (Kappa 0.414, P < .001) and fair between ACC/AHA Estimator and WHO/ISH (Kappa 0.223, P < .001) and between ACC/AHA Estimator and FRS (Kappa 0.301, P < .001). The considerable variation in prediction of high risk using the 3 tools may lead to underutilization of evidence-based therapy. This underscores the dire need for the development of risk prediction tools derived from our own Nigerian population.

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Accession: 059527431

Download citation: RISBibTeXText

PMID: 29050979

DOI: 10.1016/j.jacl.2017.09.004

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