Prevention of cardiovascular disease (CVD) requires timely identification of people who are at increased risk in order to target effective dietary, lifestyle, or pharmacotherapeutic intervention—or a combination of the 3. Risk factors for CVD are well understood, but the relative impact of each factor on an individual’s overall risk is difficult to accurately quantify, making a validated CVD risk calculator an important clinical tool.
Despite numerous available CVD risk calculators, one best tool has yet to emerge. This state of affairs has limited the ability of front-line providers who are tasked with primary prevention of CVD—including family physicians (FPs)—to provide the best evidence-based recommendations to patients.
Implications of CVD risk assessment
Baseline CVD risk assessment is the cornerstone of recommendations for primary prevention of CVD, including aspirin and statin therapy. Interventions to lower CVD risk are of greatest benefit to those at highest risk at initiation of therapy. Overall, statins reduce the risk of a first cardiovascular event in otherwise healthy people by approximately 25% over 10 years.1 Because relative risk reduction is fairly consistent across different levels of absolute risk, a 25% relative reduction confers more actual benefit if risk starts at, say, 40% than at 10%.2 In that example, the same 25% reduction in relative risk results in 1) an absolute risk reduction of 10% when risk starts at 40%, compared to an absolute risk reduction of 2.5% when risk starts at 10% and 2) a number needed to treat (NNT) of, respectively, 10 and 40 (over 10 years).
Identifying a person with an elevated risk of developing CVD has multiple implications. Ideally, that patient is motivated to pursue positive therapeutic lifestyle modifications and make changes that positively affect long-term CVD risk. Conversely, that asymptomatic person identified as at elevated risk also becomes a patient with a medical problem that might adversely affect insurance premiums and self-esteem, and may trigger the use of medications with cost and potential adverse effects. Although the benefit of preventive therapy is greater for a patient at higher risk of disease, the harm of a therapy is relatively constant across all risk groups. Accurately discriminating high and low risk of CVD is, therefore, imperative.
The venerable Framingham risk score
Cardiovascular risk prediction has its roots in the late 1940s, when primary risk factors for CVD were not well-understood, with the inception of the Framingham Heart Study. (A greater understanding of CVD risk today notwithstanding, coronary artery disease [CAD] remains the leading cause of death among American adults.) In the late 1940s, blood pressure (BP) was recognized as the single most useful variable for identifying people at high risk of CVD; other variables were understood to be predictive as well. A composite score—the Framingham Risk Score (FRS)—was thereby developed to calculate the probability that CVD would occur over 8 years in a person who was initially free of such disease.3
The original FRS included glucose intolerance and left ventricular hypertrophy (LVH) identified by electrocardiography (EKG) in its algorithm.3 Other, older algorithms also include a family history of premature CVD. In each risk calculator, these variables are treated as dichotomous (Yes or No), but actual risk associated with each variable is in fact more along a continuum. It is now well-recognized that the sensitivity of EKG for accurately detecting LVH is relatively low; more recent algorithms no longer include this component. A family history of premature CVD variably contributes to an individual’s CVD risk; however, its true impact is nearly impossible to accurately quantify, so this variable is also not included in more modern risk calculators.
Caution: The FRS has meaningful limitations
Although the original Framingham cohort has been expanded multiple times since its inception, clinicians and researchers continue to express concern that the predominantly white, middle-class Framingham, Massachusetts, population might not be representative of the United States in general—which would limit the accuracy of the FRS predictive tool when it is applied to a more diverse population. Furthermore, cholesterol-lowering medications were not available when the FRS was first developed. The FRS, therefore, might not accurately estimate risk in more modern populations, in whom aggressive modification of CVD risk factors has resulted in a lower overall rate of atherosclerotic CVD than when the FRS was developed.4
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