Robyn L McClelland1, Neal W Jorgensen2, Matthew Budoff3, Michael J Blaha4, Wendy S Post4, Richard A Kronmal2, Diane E Bild5, Steven Shea6, Kiang Liu7, Karol E Watson8, Aaron R Folsom9, Amit Khera10, Colby Ayers11, Amir-Abbas Mahabadi12, Nils Lehmann13, Karl-Heinz Jöckel13, Susanne Moebus13, J Jeffrey Carr14, Raimund Erbel12, Gregory L Burke15. 1. Department of Biostatistics, University of Washington, Seattle, Washington. Electronic address: rmcclell@u.washington.edu. 2. Department of Biostatistics, University of Washington, Seattle, Washington. 3. Division of Cardiology, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Los Angeles, California. 4. Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Baltimore, Maryland. 5. Patient-Centered Outcomes Research Institute, Washington, DC. 6. Departments of Medicine and Epidemiology, Columbia University, New York, New York. 7. Department of Preventive Medicine, Northwestern University Medical School, Chicago, Illinois. 8. Division of Cardiology, UCLA School of Medicine, Los Angeles, California. 9. Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota. 10. Division of Cardiology, UT Southwestern Medical Center, Dallas, Texas. 11. Division of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. 12. University Clinic Essen, Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), Essen, Germany. 13. Institute of Medical Informatics, Biometry, and Epidemiology, University Clinic Essen, University of Duisburg, Essen, Germany. 14. Department of Radiology and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee. 15. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Abstract
BACKGROUND: Several studies have demonstrated the tremendous potential of using coronary artery calcium (CAC) in addition to traditional risk factors for coronary heart disease (CHD) risk prediction. However, to date, no risk score incorporating CAC has been developed. OBJECTIVES: The goal of this study was to derive and validate a novel risk score to estimate 10-year CHD risk using CAC and traditional risk factors. METHODS: Algorithm development was conducted in the MESA (Multi-Ethnic Study of Atherosclerosis), a prospective community-based cohort study of 6,814 participants age 45 to 84 years, who were free of clinical heart disease at baseline and followed for 10 years. MESA is sex balanced and included 39% non-Hispanic whites, 12% Chinese Americans, 28% African Americans, and 22% Hispanic Americans. External validation was conducted in the HNR (Heinz Nixdorf Recall Study) and the DHS (Dallas Heart Study). RESULTS: Inclusion of CAC in the MESA risk score offered significant improvements in risk prediction (C-statistic 0.80 vs. 0.75; p < 0.0001). External validation in both the HNR and DHS studies provided evidence of very good discrimination and calibration. Harrell's C-statistic was 0.779 in HNR and 0.816 in DHS. Additionally, the difference in estimated 10-year risk between events and nonevents was approximately 8% to 9%, indicating excellent discrimination. Mean calibration, or calibration-in-the-large, was excellent for both studies, with average predicted 10-year risk within one-half of a percent of the observed event rate. CONCLUSIONS: An accurate estimate of 10-year CHD risk can be obtained using traditional risk factors and CAC. The MESA risk score, which is available online on the MESA web site for easy use, can be used to aid clinicians when communicating risk to patients and when determining risk-based treatment strategies.
BACKGROUND: Several studies have demonstrated the tremendous potential of using coronary artery calcium (CAC) in addition to traditional risk factors for coronary heart disease (CHD) risk prediction. However, to date, no risk score incorporating CAC has been developed. OBJECTIVES: The goal of this study was to derive and validate a novel risk score to estimate 10-year CHD risk using CAC and traditional risk factors. METHODS: Algorithm development was conducted in the MESA (Multi-Ethnic Study of Atherosclerosis), a prospective community-based cohort study of 6,814 participants age 45 to 84 years, who were free of clinical heart disease at baseline and followed for 10 years. MESA is sex balanced and included 39% non-Hispanic whites, 12% Chinese Americans, 28% African Americans, and 22% Hispanic Americans. External validation was conducted in the HNR (Heinz Nixdorf Recall Study) and the DHS (Dallas Heart Study). RESULTS: Inclusion of CAC in the MESA risk score offered significant improvements in risk prediction (C-statistic 0.80 vs. 0.75; p < 0.0001). External validation in both the HNR and DHS studies provided evidence of very good discrimination and calibration. Harrell's C-statistic was 0.779 in HNR and 0.816 in DHS. Additionally, the difference in estimated 10-year risk between events and nonevents was approximately 8% to 9%, indicating excellent discrimination. Mean calibration, or calibration-in-the-large, was excellent for both studies, with average predicted 10-year risk within one-half of a percent of the observed event rate. CONCLUSIONS: An accurate estimate of 10-year CHD risk can be obtained using traditional risk factors and CAC. The MESA risk score, which is available online on the MESA web site for easy use, can be used to aid clinicians when communicating risk to patients and when determining risk-based treatment strategies.
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