Cited by 7,491Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk FactorsLipoproteins and Cardiovascular Health

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General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study

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D’Agostino, Ralph B., Vasan, Ramachandran S., Pencina, Michael J., Wolf, Philip A., Cobain, Mark, Massaro, Joseph M., Kannel, William B.·2008·Circulation

DOI 10.1161/circulationaha.107.699579

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Abstract

Background— Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. Methods and Results— We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions (“general CVD” algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P <0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non–laboratory-based predictors. Conclusions— A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.

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Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Summary of the second report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II)
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III)
Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011
Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
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Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
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Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data
Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68770-9 · Cited by 5,630
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project
Conroy, R. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00114-3 · Cited by 5,290
Heart Disease and Stroke Statistics—2007 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Rosamond, W., Flegal, K., Friday, G., Furie, K., Go, A., Greenlund, K., Haase, N., Ho, M., Howard, V., Kissela, B., et al. (2007) · Circulation · doi:10.1161/circulationaha.106.179918 · Cited by 3,277

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10.1016/S0140-6736(05)70240-3 · DOI
10.1161/circ.83.1.1984895 · DOI
10.1161/circ.105.3.310 · DOI
10.1161/circ.97.18.1837 · DOI
10.1161/str.22.3.2003301 · DOI
10.1161/circ.96.1.44 · DOI
10.1161/circ.100.9.988 · DOI

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Self-citation

Authors: D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B.
Affiliations: Boston University (US)
ORCID: D’Agostino, Ralph B.
Vasan, Ramachandran S.
Pencina, Michael J.
Wolf, Philip A.
Massaro, Joseph M.
Venue: Circulation
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2008-02-12
Volume: 117
Issue: 6
Pages: 743-753
ISSN: 0009-7322 (ISSN-L)
1524-4539
DOI: 10.1161/circulationaha.107.699579
OpenAlex: W2148092884
Cited by: 7,491
References: 52
Citations by year: 2026: 491 · 2025: 567 · 2024: 552 · 2023: 511 · 2022: 529 · 2021: 592 · 2020: 562 · 2019: 454 · 2018: 475 · 2017: 452 · 2016: 385 · 2015: 389 · 2014: 391 · 2013: 323 · 2012: 294
FWCI: 169.19
Type: journal article
Language: en
Update notices: correction (2008-07-22)
Open access: bronze
doi.org
Locations: Circulation (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (n01hc25195-41-0-1)
National Heart, Lung, and Blood Institute (2K24 HL 04334, N01‐HC25195, K24HL004334)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
Keywords: cardiovascular risk prediction, atherosclerotic cardiovascular disease, Framingham risk score, coronary artery disease, stroke, peripheral arterial disease, heart failure, traditional risk factors, cholesterol, blood pressure, smoking, diabetes mellitus, primary care
MeSH terms: Adult, Aged, Algorithms, Cardiovascular Diseases, Female, Humans, Longitudinal Studies, Male, Middle Aged, Primary Health Care, Risk Factors, Sex Factors, Multivariate Analysis, Proportional Hazards Models, Risk Assessment

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General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study

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