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Absolute risk assessment in the clinical setting
Absolute Risk Assessment in the Clinical Setting Lori Mosca, MD, MPH, PhD
I
t is well known that coronary heart disease (CHD) is multifactorial and that the CHD risk associated with a given factor varies as a function of the number and intensity of other concurrent risk factors.1 Data from the Framingham Heart Study showed that an incremental increase in CHD risk is associated with additional cardiovascular disease (CVD) risk factors.2 Recently it was demonstrated that nonlipid risk factors increase the risk for CHD associated with lipoprotein(a) (Lp[a]), and risk associated with Lp(a) also depends on the ratio between total cholesterol and high-density lipoprotein (HDL) concentrations.3 Although it is unclear whether Lp(a) is an independent risk factor for CVD, these data illustrate the complexity of risk-factor interactions. In assessments of disease risk, absolute risk is the probability that an individual with a certain set of characteristics will develop disease within a fixed period. A strong rationale for including multiple risk factors in the assessment of a patient’s absolute risk for CHD is that information about a constellation of risk factors allows better prediction than a single risk factor.1 Moreover, this method of quantitative risk assessment allows the identification of a small segment of the population among which a large percentage of CVD events will occur. This information is critical in developing strategies for aggressive risk factor management.4 Absolute risk reduction can be used to evaluate the benefit of an intervention, such as the reduction in the number of cardiovascular events associated with lipid-lowering therapy compared with standard therapy or placebo. Furthermore, absolute risk reduction can be evaluated in relation to the level of baseline risk. The absolute benefit of therapy is usually greatest in individuals with the highest level of risk at baseline. Prediction algorithms to determine the absolute risk for CHD have been based primarily on data from the Framingham Heart Study.2 Several scoring systems, comprising 5–15 CHD risk factors, are currently available.5 Moreover, prediction equations are available for patients with existing CVD as well as for individuals free of disease.6 Future CHD risk may be categorized from low to very high or presented as the probability of a coronary event within a specified period (e.g., 10 years).7 The American Heart Association has developed risk factor
From the Department of Internal Medicine, Division of Cardiology, University of Michigan Health System, Ann Arbor, Michigan, USA. Dr. Mosca is funded by Clinical Investigator Development Award NHLBI KO8 03681 from the National Institutes of Health. Requests for reprints should be addressed to Lori Mosca, MD, MPH, PhD, University of Michigan Health System, Preventive Cardiology Program, Department of Internal Medicine, Division of Cardiology, 24 Frank Lloyd Wright Drive, Ann Arbor, Michigan 48103-0363. © 1999 by Excerpta Medica, Inc. All rights reserved.
scoring systems for use by the general public as well as algorithms for use by physicians and other healthcare providers (Figure 1).8 A recent model uses sex-specific equations to predict CHD risk according to age, the presence of diabetes, smoking, Joint National Committee (JNC)-VI blood pressure categories, and National Cholesterol Education Program (NCEP) total and HDL cholesterol categories.8 Methods for assessing the absolute risk for CHD have several uses and advantages. In addition to providing prognostic information, absolute risk assessment may be used to facilitate risk factor management in clinical practice. Clinicians may use this information to prioritize risk reduction therapy and to help determine the aggressiveness of such therapy. Absolute risk assessment may be used to estimate the potential reduction in risk associated with a change in risk factor status, and such data can be used to facilitate patient education. Although estimates of potential risk reduction do not take into account the effectiveness and potential adverse effects of therapies used in daily clinical practice, they can have a motivational effect on patients. Absolute risk assessment can also help to validate national practice guidelines that categorize individuals by level of baseline risk. For example, a prediction model demonstrated that current NCEP guidelines more accurately predict 12-year CHD mortality risk than do older guidelines.9 In some countries, prediction equations are used to set a threshold for treatment. A joint European task force recently released recommendations for the use of absolute multifactorial CHD risk as a guide for lifestyle interventions and drug treatments for the prevention of CHD.7 To guide the selective use of proven drug therapies for CHD prevention, this report suggested a threshold level of CHD risk of 20% over the next 10 years or CHD risk exceeding 20% if projected to age 60 years. Another potential use of absolute risk assessment is in the development of clinical research protocols, especially the establishment of eligibility criteria that provide an estimate of the probability that a particular event will occur during the study. In addition to its advantages, absolute risk assessment also has a number of limitations.10 For example, such assessment may underestimate future CHD risk in younger individuals. Because age is a strong predictor of cardiovascular events, the absolute risk for CHD is usually greatest in the elderly and is often low in younger individuals, who may derive long-term benefit from a change in lifestyle or in other CHD risk factors at the time of assessment. Cumulative absolute CHD risk to age 60 years may be a more appropriate measure than 10-year 0002-9343/99/$20.00 7S PII S0002-9343(99)00135-7
A Symposium: Absolute Risk Assessment in the Clinical Setting/Mosca
Figure 1. Stepwise risk factor prediction chart for categorizing risk of coronary heart disease. ECG-LVH ⫽ electrocardiogram–left ventricular hypertrophy; HDL ⫽ high-density lipoprotein; HDL-C ⫽ HDL cholesterol; SBP ⫽ systolic blood pressure; Total-C ⫽ total cholesterol. (䉷 1990, American Heart Association. Reprinted with permission.)
risk in a 30-year-old, emphasizing the risk of premature CHD. Furthermore, difficulty in interpreting the results may limit the use of absolute risk assessment by physicians. The level of absolute risk varies considerably depending on whether the model includes angina or is limited to a “hard” endpoint, such as nonfatal myocardial infarction or coronary death. Levels of absolute risk will be higher if a CVD or total-mortality endpoint is used. Moreover, secular trends in the treatment of CVD may have an impact on the accuracy of the model used. For example, if data for regression equations were derived from populations in the prethrombolytic era, the results may not be applicable to more contemporary cohorts with a greater likelihood of survival. However, even though the level of absolute risk may not be correct, the rank order of individuals will often be valid. The extent to which new risk factors provide added value to traditional models is not well defined. The interpretation of results is also influenced by the choice of a comparison group. Whereas 8S August 23, 1999 THE AMERICAN JOURNAL OF MEDICINE威
most models use persons at average risk as the reference group in assessments of individual absolute risk , those at low risk may be a more appropriate comparison group. Perhaps the greatest challenge of absolute risk assessment in CVD is to increase its clinical utility. Assessment algorithms can be difficult to incorporate into a busy practice. Moreover, prevention may not receive the same level of attention as urgent healthcare needs. Target risk factor levels often are not achieved in practice despite knowledge about future risk and the availability of effective therapies. Evidence for undertreatment of risk factors is abundant. Data from the third National Health and Nutrition Examination Survey indicate that 15.7 million Americans have hypercholesterolemia and fewer than two risk factors for CHD, and that an additional 26.6 million Americans have hypercholesterolemia and two or more CHD risk factors.11 A striking 82.5% of adults with CHD did not meet the current NCEP target of ⬍100 mg/dL for low-density lipoprotein cholesterol, reflecting the need for more aggressive risk reduction in the setting
Volume 107 (2A)
A Symposium: Absolute Risk Assessment in the Clinical Setting/Mosca
of established CVD as well as among high-risk individuals. 7.
REFERENCES 1. Gordon T, Kannel WB. Multiple risk functions for predicting coronary heart disease: the concept, accuracy and application. Am Heart J. 1982;103:1031–1039. 2. Wilson PWF, Castelli, WP, Kannel WB. Coronary risk prediction in adults (The Framingham Heart Study). Am J Cardiol. 1987;59:91G–94G. 3. Hopkins PN, Wu LL, Hunt SC, James BC, Vincent GM, Williams RR. Lipoprotein(a) interactions with lipid and nonlipid risk factors in early familial coronary artery disease. Arterioscler Thromb Vasc Biol. 1997;17:2783–2792. 4. Grundy SM, Balady GJ, Criqui MH, et al. Primary prevention of coronary heart disease: guidance from Framingham—a statement for healthcare professionals from the AHA Task Force on Risk Reduction. Circulation. 1998;97:1876 –1887. 5. Higgins M. How to estimate and reduce coronary heart disease risk in women. Cardiol Rev. 1997;5:199 –207. 6. Califf RM, Armstrong PW, Carver JR, D’Agostino RB,
8.
9.
10.
11.
August 23, 1999
Strauss WE. Task Force 5: stratification of patients into high, medium, and low risk subgroups for purposes of risk factor management. J Am Coll Cardiol. 1996;27:964 –1047. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K. Prevention of coronary heart disease in clinical practice: summary of recommendations of the Second Joint Task Force of European and Other Societies on Coronary Prevention. Atherosclerosis. 1998;140:197–198. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847. Grover SA, Coupla L, Hu X-P. Identifying adults at increased risk of coronary disease: How well do the current cholesterol guidelines work? JAMA. 1995;274:801– 806. Greenland P, Grundy SM, Pasternak RC, Lenfant C. Problems on the pathway from risk assessment to risk reduction. Circulation. 1998;97:1761–1762 Hoerger TJ, Bala MV, Bray JW, Wilcosky TC, LaRosa J. Treatment patterns and distribution of low-density lipoprotein cholesterol levels in treatment-eligible United States adults. Am J Cardiol. 1998;82:61– 65.
THE AMERICAN JOURNAL OF MEDICINE威
Volume 107 (2A)
9S
I
t is well known that coronary heart disease (CHD) is multifactorial and that the CHD risk associated with a given factor varies as a function of the number and intensity of other concurrent risk factors.1 Data from the Framingham Heart Study showed that an incremental increase in CHD risk is associated with additional cardiovascular disease (CVD) risk factors.2 Recently it was demonstrated that nonlipid risk factors increase the risk for CHD associated with lipoprotein(a) (Lp[a]), and risk associated with Lp(a) also depends on the ratio between total cholesterol and high-density lipoprotein (HDL) concentrations.3 Although it is unclear whether Lp(a) is an independent risk factor for CVD, these data illustrate the complexity of risk-factor interactions. In assessments of disease risk, absolute risk is the probability that an individual with a certain set of characteristics will develop disease within a fixed period. A strong rationale for including multiple risk factors in the assessment of a patient’s absolute risk for CHD is that information about a constellation of risk factors allows better prediction than a single risk factor.1 Moreover, this method of quantitative risk assessment allows the identification of a small segment of the population among which a large percentage of CVD events will occur. This information is critical in developing strategies for aggressive risk factor management.4 Absolute risk reduction can be used to evaluate the benefit of an intervention, such as the reduction in the number of cardiovascular events associated with lipid-lowering therapy compared with standard therapy or placebo. Furthermore, absolute risk reduction can be evaluated in relation to the level of baseline risk. The absolute benefit of therapy is usually greatest in individuals with the highest level of risk at baseline. Prediction algorithms to determine the absolute risk for CHD have been based primarily on data from the Framingham Heart Study.2 Several scoring systems, comprising 5–15 CHD risk factors, are currently available.5 Moreover, prediction equations are available for patients with existing CVD as well as for individuals free of disease.6 Future CHD risk may be categorized from low to very high or presented as the probability of a coronary event within a specified period (e.g., 10 years).7 The American Heart Association has developed risk factor
From the Department of Internal Medicine, Division of Cardiology, University of Michigan Health System, Ann Arbor, Michigan, USA. Dr. Mosca is funded by Clinical Investigator Development Award NHLBI KO8 03681 from the National Institutes of Health. Requests for reprints should be addressed to Lori Mosca, MD, MPH, PhD, University of Michigan Health System, Preventive Cardiology Program, Department of Internal Medicine, Division of Cardiology, 24 Frank Lloyd Wright Drive, Ann Arbor, Michigan 48103-0363. © 1999 by Excerpta Medica, Inc. All rights reserved.
scoring systems for use by the general public as well as algorithms for use by physicians and other healthcare providers (Figure 1).8 A recent model uses sex-specific equations to predict CHD risk according to age, the presence of diabetes, smoking, Joint National Committee (JNC)-VI blood pressure categories, and National Cholesterol Education Program (NCEP) total and HDL cholesterol categories.8 Methods for assessing the absolute risk for CHD have several uses and advantages. In addition to providing prognostic information, absolute risk assessment may be used to facilitate risk factor management in clinical practice. Clinicians may use this information to prioritize risk reduction therapy and to help determine the aggressiveness of such therapy. Absolute risk assessment may be used to estimate the potential reduction in risk associated with a change in risk factor status, and such data can be used to facilitate patient education. Although estimates of potential risk reduction do not take into account the effectiveness and potential adverse effects of therapies used in daily clinical practice, they can have a motivational effect on patients. Absolute risk assessment can also help to validate national practice guidelines that categorize individuals by level of baseline risk. For example, a prediction model demonstrated that current NCEP guidelines more accurately predict 12-year CHD mortality risk than do older guidelines.9 In some countries, prediction equations are used to set a threshold for treatment. A joint European task force recently released recommendations for the use of absolute multifactorial CHD risk as a guide for lifestyle interventions and drug treatments for the prevention of CHD.7 To guide the selective use of proven drug therapies for CHD prevention, this report suggested a threshold level of CHD risk of 20% over the next 10 years or CHD risk exceeding 20% if projected to age 60 years. Another potential use of absolute risk assessment is in the development of clinical research protocols, especially the establishment of eligibility criteria that provide an estimate of the probability that a particular event will occur during the study. In addition to its advantages, absolute risk assessment also has a number of limitations.10 For example, such assessment may underestimate future CHD risk in younger individuals. Because age is a strong predictor of cardiovascular events, the absolute risk for CHD is usually greatest in the elderly and is often low in younger individuals, who may derive long-term benefit from a change in lifestyle or in other CHD risk factors at the time of assessment. Cumulative absolute CHD risk to age 60 years may be a more appropriate measure than 10-year 0002-9343/99/$20.00 7S PII S0002-9343(99)00135-7
A Symposium: Absolute Risk Assessment in the Clinical Setting/Mosca
Figure 1. Stepwise risk factor prediction chart for categorizing risk of coronary heart disease. ECG-LVH ⫽ electrocardiogram–left ventricular hypertrophy; HDL ⫽ high-density lipoprotein; HDL-C ⫽ HDL cholesterol; SBP ⫽ systolic blood pressure; Total-C ⫽ total cholesterol. (䉷 1990, American Heart Association. Reprinted with permission.)
risk in a 30-year-old, emphasizing the risk of premature CHD. Furthermore, difficulty in interpreting the results may limit the use of absolute risk assessment by physicians. The level of absolute risk varies considerably depending on whether the model includes angina or is limited to a “hard” endpoint, such as nonfatal myocardial infarction or coronary death. Levels of absolute risk will be higher if a CVD or total-mortality endpoint is used. Moreover, secular trends in the treatment of CVD may have an impact on the accuracy of the model used. For example, if data for regression equations were derived from populations in the prethrombolytic era, the results may not be applicable to more contemporary cohorts with a greater likelihood of survival. However, even though the level of absolute risk may not be correct, the rank order of individuals will often be valid. The extent to which new risk factors provide added value to traditional models is not well defined. The interpretation of results is also influenced by the choice of a comparison group. Whereas 8S August 23, 1999 THE AMERICAN JOURNAL OF MEDICINE威
most models use persons at average risk as the reference group in assessments of individual absolute risk , those at low risk may be a more appropriate comparison group. Perhaps the greatest challenge of absolute risk assessment in CVD is to increase its clinical utility. Assessment algorithms can be difficult to incorporate into a busy practice. Moreover, prevention may not receive the same level of attention as urgent healthcare needs. Target risk factor levels often are not achieved in practice despite knowledge about future risk and the availability of effective therapies. Evidence for undertreatment of risk factors is abundant. Data from the third National Health and Nutrition Examination Survey indicate that 15.7 million Americans have hypercholesterolemia and fewer than two risk factors for CHD, and that an additional 26.6 million Americans have hypercholesterolemia and two or more CHD risk factors.11 A striking 82.5% of adults with CHD did not meet the current NCEP target of ⬍100 mg/dL for low-density lipoprotein cholesterol, reflecting the need for more aggressive risk reduction in the setting
Volume 107 (2A)
A Symposium: Absolute Risk Assessment in the Clinical Setting/Mosca
of established CVD as well as among high-risk individuals. 7.
REFERENCES 1. Gordon T, Kannel WB. Multiple risk functions for predicting coronary heart disease: the concept, accuracy and application. Am Heart J. 1982;103:1031–1039. 2. Wilson PWF, Castelli, WP, Kannel WB. Coronary risk prediction in adults (The Framingham Heart Study). Am J Cardiol. 1987;59:91G–94G. 3. Hopkins PN, Wu LL, Hunt SC, James BC, Vincent GM, Williams RR. Lipoprotein(a) interactions with lipid and nonlipid risk factors in early familial coronary artery disease. Arterioscler Thromb Vasc Biol. 1997;17:2783–2792. 4. Grundy SM, Balady GJ, Criqui MH, et al. Primary prevention of coronary heart disease: guidance from Framingham—a statement for healthcare professionals from the AHA Task Force on Risk Reduction. Circulation. 1998;97:1876 –1887. 5. Higgins M. How to estimate and reduce coronary heart disease risk in women. Cardiol Rev. 1997;5:199 –207. 6. Califf RM, Armstrong PW, Carver JR, D’Agostino RB,
8.
9.
10.
11.
August 23, 1999
Strauss WE. Task Force 5: stratification of patients into high, medium, and low risk subgroups for purposes of risk factor management. J Am Coll Cardiol. 1996;27:964 –1047. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K. Prevention of coronary heart disease in clinical practice: summary of recommendations of the Second Joint Task Force of European and Other Societies on Coronary Prevention. Atherosclerosis. 1998;140:197–198. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847. Grover SA, Coupla L, Hu X-P. Identifying adults at increased risk of coronary disease: How well do the current cholesterol guidelines work? JAMA. 1995;274:801– 806. Greenland P, Grundy SM, Pasternak RC, Lenfant C. Problems on the pathway from risk assessment to risk reduction. Circulation. 1998;97:1761–1762 Hoerger TJ, Bala MV, Bray JW, Wilcosky TC, LaRosa J. Treatment patterns and distribution of low-density lipoprotein cholesterol levels in treatment-eligible United States adults. Am J Cardiol. 1998;82:61– 65.
THE AMERICAN JOURNAL OF MEDICINE威
Volume 107 (2A)
9S