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Audit of cardiovascular risk assessment and lipid management by Canadian primary care physicians

Milan Gupta, Michelle Tsigoulis, Mahesh Kajil, Subodh Verma and Narendra Singh

BACKGROUND: Assessment of cardiovascular risk is an essential component of preventive cardiology. Despite guideline recommendations, risk assessment remains highly variable. Further efforts to understand the knowledge and action gaps for risk assessment and lipid management in contemporary primary care are warranted.
METHODS: A retrospective chart audit of 105 physicians participating in an observational registry of healthy middle-age Canadians free of known cardiovascular disease (CVD), diabetes or treated dyslipidemia seen between 2008 and 2009 was conducted.
RESULTS: A total of 1061 patients from across Canada were reviewed. The mean age was 57 years, 61% were male, 75% were Caucasian, 39% were hypertensive and 29% had a smoking history. The Framingham risk score (FRS) was used by 61% of physicians for CVD risk assessment. Overall, 48% of patients were considered to be low risk, 40% intermediate and 12% high risk by physician assessment. This was a significant overestimation of risk (P<0.0001) compared with centrally derived FRS of 64%, 26% and 10%, respectively. Risk was overestimated more often in women (P<0.002). Statin therapy was prescribed to 390 patients (37%); however, 36.5% of patients who were eligible for treatment, according to national guidelines, were not treated, while 19.9% of noneligible patients did receive therapy.
CONClUSIONS: Despite guideline recommendations, the FRS was underutilized by Canadian primary care physicians. There was considerable discrepancy between centrally derived and physician-derived risk scores. Appropriate statin therapy appeared to be underprescribed by physicians despite an overestimation of risk. Improved dissemination of risk stratification tools and guideline recommendations are needed to optimize CVD risk reduction in primary care.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado.
 
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