CLINICAL RESEARCH: CORONARY ARTERY DISEASE
The distribution of 10-Year risk for coronary heart disease among U.S. adults
Findings from the National Health and Nutrition Examination Survey III
Earl S. Ford, MD, MPH*,*,
Wayne H. Giles, MD, MS* and
Ali H. Mokdad, PhD*
* Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Manuscript received June 30, 2003;
revised manuscript received November 24, 2003,
accepted November 25, 2003.
* Reprint requests and correspondence: Dr. Earl S. Ford, Centers for Disease Control and Prevention, 4770 Buford Highway, MS K66, Atlanta, Georgia 30341, USA.
eford{at}cdc.gov
 |
Abstract
|
|---|
OBJECTIVES: We sought to establish the distribution of the 10-year risk for coronary heart disease (CHD) among U.S. adults.
BACKGROUND: Risk assessment for CHD was developed to provide clinicians with a tool to estimate the absolute risk of developing CHD. More recently, risk assessment is increasingly being incorporated into guidelines for diagnostic testing and treatment. Yet, little is known about the 10-year risk distribution for CHD among adults in the U.S. based on these risk assessment tools.
METHODS: We applied the risk prediction algorithm used by the National Cholesterol Education Program Adult Treatment Panel III guidelines to data from 13,769 participants (representing 157,366,716 U.S. adults) age 20 to 79 years in the Third National Health and Nutrition Examination Survey (1988 to 1994).
RESULTS: Among participants without self-reported CHD (heart attack and angina pectoris), stroke, peripheral vascular disease, and diabetes, 81.7% (140 million adults) had a 10-year risk for CHD of <10%, 15.5% (23 million adults) of 10% to 20%, and 2.9% (4 million adults) of >20%. The proportion of the participants with a 10-year risk for CHD of >20% increased with advancing age and was higher among men than among women but varied little with race or ethnicity.
CONCLUSIONS: Our results help to define the distribution of 10-year risk for CHD among U.S. adults.
|
Abbreviations and Acronyms
| | BP | = blood pressure | | CHD | = coronary heart disease | | NCEP/ATP III | = National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) | | NHANES | = National Health and Nutrition Examination Survey |
|
Risk prediction for coronary heart disease (CHD) was developed to help clinicians in estimating a patient's absolute risk for developing CHD. The earliest such effort was done by Framingham Heart study investigators, and these risk equations have been periodically updated (1,2). The National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) (NCEP/ATP III) has incorporated the use of risk assessment to provide clinicians with guidelines to treat dyslipidemia (3). The NCEP/ATP III adopted a modification of the risk prediction algorithm from the Framingham Heart study (2) that incorporates a patient's age, total cholesterol concentration, high-density lipoprotein cholesterol concentration, smoking status, and systolic blood pressure (BP) to estimate a person's 10-year risk for developing CHD. Three levels of risk were defined: <10%, 10% to 20%, and >20%. The NCEP/ATP III considered these levels of risk within a broader framework of risk assessment that included establishing the presence of CHD and risk equivalents for CHD as well as risk factor counting. Patients with CHD or a CHD risk equivalent are considered to be at very high risk for sustaining an acute cardiovascular event.
More recently, risk assessment has been used to guide clinicians in performing diagnostic testing (4). In addition, risk assessment may serve as a motivational tool. Thus, the use of CHD risk assessment in developing guidelines may be widening. Consequently, estimates of the numbers of people in each of the three categories of risk may be useful. To determine the distribution of 10-year risk for CHD among adults in the U.S. as proposed by NCEP/ATP III, we examined data from the Third National Health and Nutrition Examination Survey (NHANES III).
|
Methods
|
|---|
Between 1988 and 1994, a representative sample of the civilian, non-institutionalized U.S. population was recruited into NHANES III using a multistage, stratified sampling design (5,6). After an interview in the home, participants were invited to attend a morning, afternoon, or evening examination. The survey received human subject approval from the Centers for Disease Control and Prevention.
The NCEP/ATP III risk prediction algorithm scoring approach has separate risk functions for men and for women. Total cholesterol concentration and high-density lipoprotein cholesterol, after the precipitation of other lipoproteins with a heparin-manganese chloride mixture, were measured using a Hitachi 704 Analyzer (Boehringer Mannheim Diagnostics, Indianapolis, Indiana). Three BP readings were obtained in the mobile examination center. The average of the second and third systolic BP and diastolic BP readings were used in the analyses. Participants were asked whether they were currently using antihypertensive medication. Participants were considered current smokers if they reported smoking at least 100 cigarettes during their life and were currently smoking.
To define CHD, we used self-reported heart attack and angina pectoris derived from a series of questions. To define CHD risk equivalents, we used self-reported stroke and diabetes; peripheral vascular disease was determined from a series of questions.
Statistical analyses.
The NCEP/ATP III risk prediction algorithm was proposed for people age 20 to 79 years; consequently, only participants in this age range were included in the analyses. Analyses were limited to participants who attended the examination. We calculated estimates using the sampling weights so that the estimates were representative of the civilian, non-institutionalized U.S. population. Age-adjustment was done using the direct method and the age-distribution for the year 2000. To account for the complex sampling design, all analyses were conducted by using Software for the Statistical Analysis of Correlated Data (SUDAAN, Research Triangle Institute, Research Triangle Park, North Carolina) to obtain proper variance estimates.
|
Results
|
|---|
A total of 13,769 participants (6,433 men and 7,336 women) had complete information to calculate their 10-year risk for CHD. An additional 289 participants without complete information reported having CHD or a CHD risk equivalent (diabetes, peripheral vascular disease, or stroke). Compared with women, men were younger, had lower concentrations of total cholesterol and high-density lipoprotein cholesterol, had higher systolic BP, were less likely to use antihypertensive medication, and were more likely to smoke (Table 1).
View this table:
[in this window]
[in a new window]
|
Table 1 Age-Adjusted Descriptive Information for Variables Included in the 10-Year Risk Estimates for Coronary Heart Disease, National Health and Nutrition Examination Survey III, 1988 to 1994
|
|
Among participants without CHD (self-reported myocardial infarction or angina pectoris) or a CHD risk equivalent (diabetes, peripheral vascular disease, or stroke), 81.7% had a 10-year risk for CHD of <10%, 15.5% of 10% to 20%, and 2.9% of >20% (Table 2). The risk increased strongly with advancing age, was greater among men than women, and did not differ much among the four racial or ethnic groups. We also calculated the risk distribution after assigning participants with CHD or a CHD risk equivalent to the highest risk category (Table 3, Fig. 1). After adjusting for age, 72.6% of participants had a 10-year risk for CHD of <10%, 11.9% of 10% to 20%, and 15.6% of >20%.
View this table:
[in this window]
[in a new window]
|
Table 2 Age-Specific and Age-Adjusted Distribution Risk for Coronary Heart Disease Among United States Adults Age  20 Years Without Self-Reported Coronary Heart Disease or a Coronary Heart Disease Equivalent,* National Health and Nutrition Examination Survey III, 1988 to 1994
|
|
View this table:
[in this window]
[in a new window]
|
Table 3 Age-Specific and Age-Adjusted Distribution of Risk for Coronary Heart Disease Among U.S. Adults Age 20 Years After Including People With Self-Reported Coronary Heart Disease or Coronary Heart Disease Risk Equivalents* in the Highest Risk Category, National Health and Nutrition Examination Survey III, 1988 to 1994
|
|
View larger version (28K):
[in this window]
[in a new window]
|
Figure 1 Age-specific distribution of risk for coronary heart disease (CHD) among U.S. adults age 20 years after including people with self-reported CHD or CHD risk equivalents (history of diabetes mellitus, peripheral vascular disease, or stroke) in the highest risk category, National Health and Nutrition Examination Survey III, 1988 to 1994. Hatched bars = >20%; solid bars = 10% to 20%; open bars = <10%.
|
|
Although the NCEP/ATP III report defined intermediate risk as 10% to 20%, a range of 6% to 20% has also been used. Therefore, we recalculated the estimates in Tables 2 and 3 using the following categories: <6%, 6% to 20%, and >20%. Among participants without CHD or a CHD risk equivalent, 72.8% (men: 55.0%; women: 88.4%) had a 10-year risk of <6%, 24.4% (men: 39.7%; women: 10.8%) had a risk of 6% to 20%, and 2.9% (men: 5.3%; women: 0.9%) had a risk of >20%. When participants with CHD or a CHD risk equivalent were assigned to the highest risk category, 65.4% (men: 51.7%; women: 77.8%) had a 10-year risk of <6%, 19.1% (men: 31.0%; women: 8.1%) had a risk of 6% to 20%, and 15.6% (men: 17.3%; women: 14.1%) had a risk of >20%.
By applying the age-specific proportions of participants in each risk stratum to the year 2000 U.S. population age 20 to 79 years without CHD or a CHD equivalent, as we defined it, we estimated that about 140 million people had a 10-year risk for CHD of <10%, 23 million of 10% to 20%, and 4 million of >20%. In addition, another 24 million people had CHD or a CHD equivalent.
Among participants who had a CHD risk equivalent or >20% 10-year risk for developing CHD or sustaining a cardiovascular event, 59.2% had self-reported diabetes, 14.8% had a stroke, 5.3% had peripheral vascular disease, and 36.1% had a 10-year risk for CHD >20% (Table 4). When the analysis was limited to participants who had fasted 8 h and had a plasma glucose measurement, 65.8% of participants had diabetes (self-report or 126 mg/dl), 12.2% had stroke, 4.6% had peripheral vascular disease, and 32.8% had a 10-year risk for CHD >20%.
View this table:
[in this window]
[in a new window]
|
Table 4 Distribution of CHD Risk Equivalents Among 3,026* U.S. Adults Age 20 Years With Any Such Risk Equivalent, National Health and Nutrition Examination Survey III, 1988 to 1994
|
|
|
Discussion
|
|---|
The results from our analyses may help to better define the percentages and numbers of U.S. adults at three levels of risk for CHD during a 10-year interval. Such information may be helpful to those developing diagnostic and treatment guidelines for topics related to CHD. These findings may also help in estimating costs associated with such guidelines. In addition, this information may hold promise for surveillance purposes. Health care organizations use prediction models for various purposes including identifying members at increased risk for incurring high costs and planning care delivery (7,8). Although risk prediction using information from their own memberships is of greater practical use to those organizations, results such as ours may still be useful for making comparisons with national data.
Several issues may have affected the accuracy of our numbers. The definitions used in this study for CHD and CHD equivalent were based on self-reported data provided by the participants. Such data may underestimate the true prevalence of these conditions. If we had been able to detect these conditions more accurately, our estimates of the percentage of participants in each risk stratum would likely have been lower. For example, if the prevalence of CHD or CHD equivalent were twice as large as our estimates, about 123 million people would have had a 10-year risk for CHD of <10%, 16.5 million of 10% to 20%, and 3 million of >20%. Smoking was self-reported, which likely underestimated the true prevalence of smoking. This, in turn, may have resulted in an underestimate of the percentage of participants who had a 10-year risk for CHD of 10% to 20% or >20%.
The Framingham coronary disease prediction algorithm that formed the basis for the NCEP/ATP III risk scoring algorithm used age, low-density lipoprotein cholesterol concentration, total cholesterol concentration, high-density lipoprotein cholesterol concentration, BP, diabetes, and smoking status and was developed for people age 30 to 74 years (2). Several changes to this algorithm were adopted by NCEP/ATP III. The age range was broadened to include people age 20 to 79 years. Low-density lipoprotein cholesterol was not included. Different high-density lipoprotein cholesterol categories were used. Points assigned for BP depended on treatment status, which was not considered by the Framingham algorithm. Finally, points assigned for smoking status were dependent on age, whereas in Framingham age was not a factor. Because diabetes was considered a risk equivalent by the NCEP/ATP III, it was not incorporated in the risk-scoring algorithm.
The NCEP/ATP III describes two approaches to risk assessment in persons without CHD or CHD risk equivalents. The principal approach consists of first counting the number of risk factors using five factors: cigarette smoking, hypertension, low-, high-density lipoprotein cholesterol concentration, family history of premature CHD, and age. For people with 2 risk factors, NCEP/ATP III recommends calculating the 10-year risk for CHD. The second approach consists of first calculating the 10-year risk score for CHD and, in people with a 10-year risk <10%, count the number of risk factors. People with CHD or a CHD risk equivalent are automatically considered to be at highest risk under either approach. We followed the latter approach but did not count risk factors in participants with a 10-year risk for CHD of <10%, primarily because of difficulty in defining a family history of CHD as described by NCEP/ATP III.
The NHANES III provided the most recent national data to estimate risk, but newer data for some of the risk factors needed for risk estimation is becoming available. The U.S. population has grown older. Cholesterol concentrations have changed little from the 1988 to 1994 period to the 1999 to 2000 period (9). National trends in high-density lipoprotein cholesterol concentration remain unknown. A significant increase in hypertension has occurred during this time, and the percentage of people with hypertension who are using antihypertensive medications has increased (10). Cigarette smoking decreased during the early part of the 1990s before leveling off (11). Thus, the net effect of the trends of risk factors on the population risk distribution of cardiovascular disease is difficult to predict.
The use of risk categories instead of equations may yield less accurate estimates of absolute risk. Newer analytical techniques may provide more accurate risk estimates in the future (12). In addition, most risk equations and the NCEP/ATP III risk prediction algorithm are based on a limited set of variables. Factors—such as obesity, physical activity, diabetes, triglyceride concentration, family history of CHD, and so on—are not included in the NCEP/ATP III risk calculation. Furthermore, emerging risk factors for CHD, such as C-reactive protein concentration, may improve risk prediction. In the future, risk algorithms may be refined and incorporate additional risk factors.
The generalizability of the NCEP/ATP III risk prediction algorithm has not been tested directly. However, the Framingham risk equation that was used to develop the algorithm had been tested in several U.S. as well as European populations (13–20). In the U.S., the Framingham risk prediction algorithm appears to predict risk reasonably well among various populations but not Native Americans (17).
Few studies have provided estimates of the distribution of risk for CHD in populations. In a British study of 126 hypertensive men, 50% had a risk of 2% per year, and 28% had a risk of 3% per year based on the risk function of the Prospective Cardiovascular Münster study (21). In a study of 691 British participants age 30 to 70 years recruited from primary care practices, 8.5% had a 10-year projected risk for CHD of 30%, and 42.1% had a projected risk of 15% (22). In a study of 1,102 British men and women, the 10-year risk for CHD of 30% was 6.5% among men and 0.8% among women (19). The 10-year risk for 15% to 29% was 34.8% for men and 19.4% for women. In an analysis of data from the Prospective Cardiovascular Münster study, 3.9% to 8.4% of men age 35 to 65 years had a 10-year risk of >20% (12). The variability of estimates occurred because of the different approaches used for calculating risk. Our analysis of NHANES III data of participants age 20 to 79 years showed that 5.3% of men and 0.9% of women had a 10-year risk for CHD of >20%, and 28.7% of men and 4.3% of women had a 10-year risk for CHD of 10% to 20%. The large difference between the sexes in our analyses was consistent with findings by Rabindranath et al. (19).
In conclusion, we have provided estimates of the distribution of 10-year CHD risk among U.S. adults and have estimated the numbers of U.S. adults in each risk stratum. The risk distribution differed significantly among age groups and among men and women. Relatively little racial or ethnic variation was found. As the various risk stratification schemes evolve, new estimates will be needed. Hopefully, our estimates will provide useful information to researchers and clinicians who develop guidelines for diagnostic testing and treatment as well as policy makers.
|
References
|
|---|
1. Coronary Risk Handbook: Estimating Risk of Coronary Heart Disease in Daily Practice. New York, NY: American Heart Association, 1973:1–35
2. Wilson PW, 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[Abstract/Free Full Text]
3. National Institutes of Health. Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III): Executive Summary. NIH Publication No. 01–3670. Washington, DC: Government Printing Office, 2001.
4. Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: Application to clinical and public health practice: a statement for healthcare professionals from the centers for disease control and prevention and the american heart association. Circulation. 2003;107:499–511[Free Full Text]
5. Centers for Disease Control and Prevention. Plan and operation of the third National Health and Nutrition Examination Survey, 1988 to 1994. Vital Health Stata 1994;1.
6. Centers for Disease Control and Prevention. The Third National Health and Nutrition Examination Survey (NHANES III 1988 to 1994) reference manuals and reports (materials on CD-ROM). Bethesda, MD: National Center for Health Statistics; 1996.
7. Dove HG, Duncan I, Robb A. A prediction model for targeting low-cost, high-risk members of managed care organizations. Am J Manag Care. 2003;9:381–389[Medline]
8. Selby JV, Karter AJ, Ackerson LM, Ferrara A, Liu J. Developing a prediction rule from automated clinical databases to identify high-risk patients in a large population with diabetes. Diabetes Care. 2001;24:1547–1555[Abstract/Free Full Text]
9. Ford ES, Mokdad AH, Giles WH, Mensah GA. Serum total cholesterol concentrations and awareness, treatment, and control of hypercholesterolemia among U.S. adults: Findings from the national health and nutrition examination survey, 1999 to 2000. Circulation. 2003;107:2185–2189[Abstract/Free Full Text]
10. Hajjar I, Kotchen TA. Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988–2000. JAMA. 2003;290:199–206[Abstract/Free Full Text]
11. Ahluwalia IB, Mack KA, Murphy W, Mokdad AH, Bales VS. State-specific prevalence of selected chronic disease-related characteristics—Behavioral Risk Factor Surveillance System, 2001. MMWR. 2003;52:1–80[Medline]
12. Voss R, Cullen P, Schulte H, Assmann G. Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Munster study (PROCAM) using neural networks. Int J Epidemiol. 2002;31:1253–1262[Abstract/Free Full Text]
13. Brand RJ, Rosenman RH, Sholtz RI, Friedman M. Multivariate prediction of coronary heart disease in the Western Collaborative Group study compared to the findings of the Framingham study. Circulation. 1976;53:348–355[Abstract/Free Full Text]
14. Leaverton PE, Sorlie PD, Kleinman JC, et al. Representativeness of the Framingham risk model for coronary heart disease mortality: A comparison with a national cohort study. J Chronic Dis. 1987;40:775–784[CrossRef][Medline]
15. Knuiman MW, Vu HT. Prediction of coronary heart disease mortality in Busselton, Western Australia: An evaluation of the framingham, national health epidemiologic follow-up study, and who erica risk scores. J Epidemiol Community Health. 1997;51:515–519[Abstract/Free Full Text]
16. Liao Y, McGee DL, Cooper RS, Sutkowski MB. How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts. Am Heart J. 1999;137:837–845[CrossRef][Medline]
17. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: Results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187[Abstract/Free Full Text]
18. Game FL, Jones AF. Coronary heart disease risk assessment in diabetes mellitus—a comparison of PROCAM and Framingham risk assessment functions. Diabetes Med. 2001;18:355–359[CrossRef][Medline]
19. Rabindranath KS, Anderson NR, Gama R, Holland MR. Comparative evaluation of the new Sheffield table and the modified joint British societies coronary risk prediction chart against a laboratory based risk score calculation. Postgrad Med J. 2002;78:269–272[Abstract/Free Full Text]
20. Orford JL, Sesso HD, Stedman M, Gagnon D, Vokonas P, Gaziano JM. A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study. Am Heart J. 2002;144:95–100[CrossRef][Medline]
21. Haq IU, Ramsay LE, Jackson PR, Wallis EJ. Prediction of coronary risk for primary prevention of coronary heart disease: A comparison of methods. QJM. 1999;92:379–385[Abstract/Free Full Text]
22. Jones AF, Walker J, Jewkes C, et al. Comparative accuracy of cardiovascular risk prediction methods in primary care patients. Heart. 2001;85:37–43[Abstract/Free Full Text]
This article has been cited by other articles:
|
|
|
|
 
P. K. Shah
The SHAPE Paradigm: A Commentary
Circ Cardiovasc Qual Outcomes,
January 1, 2010;
3(1):
106 - 109.
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. K. Marma, J. D. Berry, H. Ning, S. D. Persell, and D. M. Lloyd-Jones
Distribution of 10-Year and Lifetime Predicted Risks for Cardiovascular Disease in US Adults: Findings From the National Health and Nutrition Examination Survey 2003 to 2006
Circ Cardiovasc Qual Outcomes,
January 1, 2010;
3(1):
8 - 14.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. Helfand, D. I. Buckley, M. Freeman, R. Fu, K. Rogers, C. Fleming, and L. L. Humphrey
Emerging Risk Factors for Coronary Heart Disease: A Summary of Systematic Reviews Conducted for the U.S. Preventive Services Task Force
Ann Intern Med,
October 6, 2009;
151(7):
496 - 507.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
Chia Yook Chin and S. Pengal
Cardiovascular Disease Risk in a Semirural Community in Malaysia
Asia Pac J Public Health,
October 1, 2009;
21(4):
410 - 420.
[Abstract]
[PDF]
|
|
|
|
|
|
|
F. Lopez-Jimenez, J. A. Batsis, V. L. Roger, L. Brekke, H. H. Ting, and V. K. Somers
Trends in 10-Year Predicted Risk of Cardiovascular Disease in the United States, 1976 to 2004
Circ Cardiovasc Qual Outcomes,
September 1, 2009;
2(5):
443 - 450.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. K. Marma and D. M. Lloyd-Jones
Systematic Examination of the Updated Framingham Heart Study General Cardiovascular Risk Profile
Circulation,
August 4, 2009;
120(5):
384 - 390.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. D. Berry, K. Liu, A. R. Folsom, C. E. Lewis, J. J. Carr, J. F. Polak, S. Shea, S. Sidney, D. H. O'Leary, C. Chan, et al.
Prevalence and Progression of Subclinical Atherosclerosis in Younger Adults With Low Short-Term but High Lifetime Estimated Risk For Cardiovascular Disease: The Coronary Artery Risk Development in Young Adults Study and Multi-Ethnic Study of Atherosclerosis
Circulation,
January 27, 2009;
119(3):
382 - 389.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
Y. M. Smulders, A. Thijs, and J. W. Twisk
New cardiovascular risk determinants do exist and are clinically useful
Eur. Heart J.,
February 2, 2008;
29(4):
436 - 440.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. R. Preis and C. J. O'Donnell
Coronary Heart Disease Risk Assessment by Traditional Risk Factors and Newer Subclinical Disease Imaging: Is a "One-Size-Fits-All" Approach the Best Option?
Arch Intern Med,
December 10, 2007;
167(22):
2399 - 2401.
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. G. Lakoski, P. Greenland, N. D. Wong, P. J. Schreiner, D. M. Herrington, R. A. Kronmal, K. Liu, and R. S. Blumenthal
Coronary Artery Calcium Scores and Risk for Cardiovascular Events in Women Classified as "Low Risk" Based on Framingham Risk Score: The Multi-Ethnic Study of Atherosclerosis (MESA)
Arch Intern Med,
December 10, 2007;
167(22):
2437 - 2442.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. Eichler, M. A. Puhan, and L. M. Bachmann
The Role of Statins in Primary Prevention of Cardiovascular Disease
Arch Intern Med,
May 28, 2007;
167(10):
1100 - 1100.
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. G. Keevil, M. W. Cullen, R. Gangnon, P. E. McBride, and J. H. Stein
Implications of Cardiac Risk and Low-Density Lipoprotein Cholesterol Distributions in the United States for the Diagnosis and Treatment of Dyslipidemia: Data From National Health and Nutrition Examination Survey 1999 to 2002
Circulation,
March 20, 2007;
115(11):
1363 - 1370.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. M Ridker, J. E. Buring, N. Rifai, and N. R. Cook
Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women: The Reynolds Risk Score
JAMA,
February 14, 2007;
297(6):
611 - 619.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. S. Blumenthal, E. D. Michos, and K. Nasir
Further Improvements in CHD Risk Prediction for Women
JAMA,
February 14, 2007;
297(6):
641 - 643.
[Full Text]
[PDF]
|
|
|
|
|
|
|
W. Rosamond, K. Flegal, G. Friday, K. Furie, A. Go, K. Greenlund, N. Haase, M. Ho, V. Howard, B. Kissela, et al.
Heart Disease and Stroke Statistics--2007 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Circulation,
February 6, 2007;
115(5):
e69 - e171.
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. A. Pearson
The Prevention Of Cardiovascular Disease: Have We Really Made Progress?
Health Aff.,
January 1, 2007;
26(1):
49 - 60.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. J. London
Con: Beta-Blockers Are Indicated for All Adults at Increased Risk Undergoing Noncardiac Surgery
Anesth. Analg.,
January 1, 2007;
104(1):
11 - 14.
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Thavendiranathan, A. Bagai, M. A. Brookhart, and N. K. Choudhry
Primary Prevention of Cardiovascular Diseases With Statin Therapy: A Meta-analysis of Randomized Controlled Trials
Arch Intern Med,
November 27, 2006;
166(21):
2307 - 2313.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
U. A. Ajani and E. S. Ford
Has the Risk for Coronary Heart Disease Changed Among U.S. Adults?
J. Am. Coll. Cardiol.,
September 19, 2006;
48(6):
1177 - 1182.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. M. Gotto Jr
Over-The-Counter Statins Are Worth Considering in Primary Prevention of Cardiovascular Disease
Circulation,
September 19, 2006;
114(12):
1310 - 1314.
[Full Text]
[PDF]
|
|
|
|
|
|
|
C P Chung, A Oeser, I Avalos, P Raggi, and C M Stein
Cardiovascular risk scores and the presence of subclinical coronary artery atherosclerosis in women with systemic lupus erythematosus
Lupus,
September 1, 2006;
15(9):
562 - 569.
[Abstract]
[PDF]
|
|
|
|
|
|
|
T. Thom, N. Haase, W. Rosamond, V. J. Howard, J. Rumsfeld, T. Manolio, Z.-J. Zheng, K. Flegal, C. O'Donnell, S. Kittner, et al.
Heart Disease and Stroke Statistics--2006 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Circulation,
February 14, 2006;
113(6):
e85 - e151.
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. J. Shaw, C. N. Bairey Merz, C. J. Pepine, S. E. Reis, V. Bittner, S. F. Kelsey, M. Olson, B. D. Johnson, S. Mankad, B. L. Sharaf, et al.
Insights From the NHLBI-Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study: Part I: Gender Differences in Traditional and Novel Risk Factors, Symptom Evaluation, and Gender-Optimized Diagnostic Strategies
J. Am. Coll. Cardiol.,
February 7, 2006;
47(3_Suppl_S):
S4 - S20.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. N. Bairey Merz, L. J. Shaw, S. E. Reis, V. Bittner, S. F. Kelsey, M. Olson, B. D. Johnson, C. J. Pepine, S. Mankad, B. L. Sharaf, et al.
Insights From the NHLBI-Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study: Part II: Gender Differences in Presentation, Diagnosis, and Outcome With Regard to Gender-Based Pathophysiology of Atherosclerosis and Macrovascular and Microvascular Coronary Disease
J. Am. Coll. Cardiol.,
February 7, 2006;
47(3_Suppl_S):
S21 - S29.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. J. Lancaster, S. O. Watts, and L. B. Dixon
Dietary Intake and Risk of Coronary Heart Disease Differ among Ethnic Subgroups of Black Americans
J. Nutr.,
February 1, 2006;
136(2):
446 - 451.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
I. J. Kullo, G. Li, L. F. Bielak, K. R. Bailey, P. F. Sheedy II, P. A. Peyser, S. T. Turner, and S. L. R. Kardia
Association of Plasma Homocysteine With Coronary Artery Calcification in Different Categories of Coronary Heart Disease Risk
Mayo Clin. Proc.,
February 1, 2006;
81(2):
177 - 182.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
H. M. Krumholz
The Year in Epidemiology, Health Services, and Outcomes Research
J. Am. Coll. Cardiol.,
October 4, 2005;
46(7):
1362 - 1370.
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Mora, R. F. Redberg, A. R. Sharrett, and R. S. Blumenthal
Enhanced Risk Assessment in Asymptomatic Individuals With Exercise Testing and Framingham Risk Scores
Circulation,
September 13, 2005;
112(11):
1566 - 1572.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
N. K. Choudhry and J. Avorn
Over-the-Counter Statins
Ann Intern Med,
June 7, 2005;
142(11):
910 - 913.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. E. Cassidy, L. F. Bielak, Y. Zhou, P. F. Sheedy II, S. T. Turner, J. F. Breen, P. A. Araoz, I. J. Kullo, X. Lin, and P. A. Peyser
Progression of Subclinical Coronary Atherosclerosis: Does Obesity Make a Difference?
Circulation,
April 19, 2005;
111(15):
1877 - 1882.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
Top
Abstract
Methods