Curriculum in cardiology
Concept and usefulness of cardiovascular risk profiles

https://doi.org/10.1016/j.ahj.2003.10.022Get rights and content

Abstract

Despite major advances in the diagnosis and treatment of atherosclerotic cardiovascular disease (CVD) in the past century, it remains a serious clinical and public health problem. Multivariable risk factor analysis is now commonly performed to identify high-risk candidates for CVD who need preventive measures and to seek out clues to the pathogenesis of the disease. The set of risk factors used for the former is constrained by practical considerations, and the set of risk factors used for the latter is constrained by the hypothesis being tested. This report reviews the evolution and usefulness of multivariable risk functions crafted for estimating risk of clinical manifestations of atherosclerosis and for gaining insights into their pathogenesis. Decades of evaluation of CVD risk factors by the Framingham Study led to the conclusion that CVD risk evaluation is most fruitfully appraised from the multivariable risk posed by a set of established risk factors. Such assessment is essential because risk factors seldom occur in isolation, and the risk associated with each varies widely depending on the burden of associated risk factors. About half the CVD in the general population arises from the segment with multiple marginal risk factor abnormalities. Although disease-specific profiles are available, a multivariable risk formulation for coronary disease comprised of age, sex, the total/high-density lipoprotein cholesterol ratio, systolic blood pressure, glucose intolerance, cigarette smoking, and electrocardiography-left ventricular hypertrophy is also predictive of peripheral artery disease, heart failure, and stroke because of shared risk factors. Correcting risk factors for any particular CVD has the potential to protect against ≥1 of the others. Multivariable risk stratification is now recognized as essential in efficiently identifying likely candidates for CVD and quantifying the hazard.

Section snippets

Evolution of multivariable risk formulation

Extensive epidemiological research in the past half century at the Framingham Study and elsewhere was devoted to the creation of mathematical models for predicting CVD.3, 4, 5, 6, 7, 8, 9, 10 The study was a leader in defining and quantifying the impact of risk factors.11 This ongoing 50-year study has been engaged in making periodic measurements of standard and novel risk factors in the original cohort and their offspring, and observing them for initial and recurrent CVD events for extended

Framingham perspective on risk stratification

When the Framingham Study was initiated in 1949, the prevailing concept was that we should seek out a single essential cause sufficient to produce CVD. It soon became apparent that it was only going to be possible to suggest guilt by association, which gave rise to the concept of predisposing “risk factors,” a term coined in the Framingham Study.17 In every instance, the CVD hazard imposed by any particular major risk factor varied substantially in relation to the burden of risk factors that

Mathematical predictive models

Because of the multifactorial predisposition to CVD, and the need to determine and quantify the net and joint contribution of predisposing risk factors, multivariable risk formulations were needed. The first of these was devised in the 1960s and subsequently followed by risk formulations devised on the basis of longer periods of follow-up, better predictive variables, and increasingly sophisticated statistical methods, including logistic regression, Cox proportional hazards regression, and

Framingham study disease-specific CVD risk profiles

Epidemiological research in the Framingham Study identified, for either sex and specified ages, a set of major correctable risk factors that impacted strongly and independently on the rate of development of each of the major clinical manifestations of CVD.24 These included, in addition to age and sex, blood lipid levels, blood pressure, glucose tolerance, smoking, and left ventricular hypertrophy. Although all the standard risk factors contribute powerfully to coronary disease, for

Coronary risk assessment

Statistical prediction models for coronary disease were largely made on the basis of the logistic regression model in the past; now, Cox regression and Weibull models have replaced this.7, 9, 16 Framingham Study coronary risk formulations included age, sex, blood pressure, total and HDL cholesterol levels, smoking, diabetes mellitus, and left ventricular hypertrophy.7 Age, total cholesterol level, and HDL cholesterol level were used in the equations as continuous variables, whereas smoking,

Stroke risk profile

Stroke, the most feared of the atherosclerotic sequelae of CVD risk factors, becomes a serious hazard in patients aged ≥65 years. A number of CVD risk factors and cardiac conditions have been identified that independently contribute to stroke rates. With the Cox proportional hazards regression model, these were formulated into a stroke-risk profile, including age, sex, systolic blood pressure, diabetes mellitus, cigarette smoking, echocardiography-left ventricular hypertrophy, coronary and

Peripheral artery disease

Peripheral artery disease is a hazard in patients aged ≥65 years and is also an ominous harbinger of other CVD events. Persons with intermittent claudication have a 2-to 4-fold increased risk for coronary disease, stroke, or heart failure.25 The significant independent risk factors found to be useful in devising a predictive risk profile for peripheral artery disease include age, sex, blood pressure, diabetes mellitus, cigarettes, cholesterol level, and coronary disease status.21 By using a

Heart failure

Heart failure is a terminal stage of cardiac disease, with a survival experience little different from cancer. When overtly manifested, the median survival period in patients with this condition in the Framingham Study was only 1.7 years for men and 3.2 years for women, and sudden death was a prominent feature of the mortality rate.26 Recent declines in CVD mortality have not been accompanied by a reduction in the prevalence of heart failure.27 Patients who are at high risk for heart failure

Risk assessment in patients undergoing treatment

Statistical models for estimating CVD risk from population data initially made the unwarranted assumption that risk factors in patients undergoing treatment carry a risk of CVD identical to that in patients who are not undergoing treatment. At that time, this was of no concern because available treatment was not very effective or widely used. As the Framingham cohort was observed for 5 decades, more and more effective therapy for controlling risk factors was introduced. This was recognized as

Risk appraisal for subsequent CVD

In the Framingham Study, calculation of risk appraisal functions for men and women with prior CHD or stroke at the time of examination indicates that for men, only age, log ratio of total/HDL cholesterol level, and diabetes mellitus remain in the model. In women, log systolic blood pressure and smoking remain in the model with the other variables. Risk factor scoring sheets for men did not include systolic blood pressure or cigarette smoking. For women, these variables are included (Table V).

General CVD risk profile

Although the impacts of risk factors vary from one atherosclerotic CVD entity to another, there appears to be a sufficient commonality to warrant considering all clinical varieties as a unit. Also, 1 CVD event tends to presage another, so a patient with coronary disease, for example, is substantially more likely to have a stroke, peripheral artery disease, or heart failure than patients without coronary disease.6, 25 The prospect that CVD as a group may be effectively predicted from a

Transportability of risk formulations

Caution is necessary in generalizing multivariable risk from the Framingham Study to dissimilar population samples, particularly patients with a low CHD incidence. The Framingham Study multivariable coronary disease risk factor model was tested in a variety of other population samples and found to be reasonably accurate, except when applied in areas in which the incidence of coronary disease is quite low.29, 30, 31, 32, 33 However, even in these low-incidence populations, it was possible to

Preventive implications

It is now recognized that atherosclerotic CVD is attributable to a variety of factors and has several clinical manifestations. In every instance, the hazard of a particular risk factor varies widely depending on the burden of associated accompanying risk factors. Almost half of CVD events occur in the tenth of the population at highest multivariate risk.

Single risk factor detection and correction may be worthwhile for prevention of CVD on a population basis, but is inefficient on an individual

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    Framingham Study research is supported by the National Institutes of Health/National Heart, Lung, and Blood Institute (NIH/NHLBI Contract N01-HC-38038) and the Visiting Scientist Program, which is supported by Servier Amérique.

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