Michael J Blaha1, Seamus P Whelton2, Mahmoud Al Rifai3, Zeina Dardari2, Leslee J Shaw4, Mouaz H Al-Mallah5, Kunihiro Matsushita6, Alan Rozanski7, John A Rumberger8, Daniel S Berman9, Matthew J Budoff10, Michael D Miedema11, Khurram Nasir12, Miguel Cainzos-Achirica2. 1. Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. Electronic address: mblaha1@jhmi.edu. 2. Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA. 3. Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA; Section of Cardiology, Baylor College of Medicine, Houston, Texas. 4. Weill Cornell Medical College, New York, New York, USA. 5. Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas, USA. 6. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA; Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA; Department of Internal Medicine, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA. 7. Division of Cardiology, Mount Sinai St. Luke's Hospital, New York, New York, USA. 8. Princeton Longevity Center, Princeton, New Jersey, USA. 9. Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA. 10. Department of Medicine, Harbor-UCLA Medical Center, Los Angeles, California, USA. 11. Minneapolis Heart Institute and Foundation, Minneapolis, Minneapolis, USA. 12. Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA.
Abstract
OBJECTIVES: This study compared risk discrimination for the prediction of coronary heart disease (CHD) and cardiovascular disease (CVD) deaths for the Pooled Cohort Equations (PCE), the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score (with and without coronary artery calcium [CAC]), and of simple addition of CAC to the PCE. BACKGROUND: The PCE predict 10-year risk of atherosclerotic CVD events, and the MESA Risk Score predicts risk of CHD. Their comparative performance for the prediction of fatal events is poorly understood. METHODS: We evaluated 53,487 patients ages 45 to 79 years from the CAC Consortium, a retrospective cohort study of asymptomatic individuals referred for clinical CAC scoring. Risk discrimination was measured using C-statistics. RESULTS: Mean age was 57 years, 35% were women, and 39% had CAC of 0. There were 421 CHD and 775 CVD deaths over a mean 12-year follow-up. In the overall study population, discrimination with the MESA Risk Score with CAC and the PCE was almost identical for both outcomes (C-statistics: 0.80 and 0.79 for CHD death, 0.77 and 0.78 for CVD death, respectively). Addition of CAC to the PCE improved risk discrimination, yielding the largest C-statistics. The MESA Risk Score with CAC and the PCE plus CAC showed the best discrimination among the 45% of patients with 5% to 20% estimated risk. Secondary analyses by estimated CVD risk strata showed modestly improved risk discrimination with CAC also among low- and high-estimated risk groups. CONCLUSIONS: Our findings support the current guideline recommendation to use, among available risk scores, the PCE for initial risk assessment and to use CAC for further risk assessment in a broad borderline and intermediate risk group. Also, in select individuals at low or high estimated risk, CAC modestly improved discrimination. Studies in unselected populations will lead to further understanding of the potential value of tools combining risk scores and CAC for optimal risk assessment.
OBJECTIVES: This study compared risk discrimination for the prediction of coronary heart disease (CHD) and cardiovascular disease (CVD) deaths for the Pooled Cohort Equations (PCE), the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score (with and without coronary artery calcium [CAC]), and of simple addition of CAC to the PCE. BACKGROUND: The PCE predict 10-year risk of atherosclerotic CVD events, and the MESA Risk Score predicts risk of CHD. Their comparative performance for the prediction of fatal events is poorly understood. METHODS: We evaluated 53,487 patients ages 45 to 79 years from the CAC Consortium, a retrospective cohort study of asymptomatic individuals referred for clinical CAC scoring. Risk discrimination was measured using C-statistics. RESULTS: Mean age was 57 years, 35% were women, and 39% had CAC of 0. There were 421 CHD and 775 CVD deaths over a mean 12-year follow-up. In the overall study population, discrimination with the MESA Risk Score with CAC and the PCE was almost identical for both outcomes (C-statistics: 0.80 and 0.79 for CHD death, 0.77 and 0.78 for CVD death, respectively). Addition of CAC to the PCE improved risk discrimination, yielding the largest C-statistics. The MESA Risk Score with CAC and the PCE plus CAC showed the best discrimination among the 45% of patients with 5% to 20% estimated risk. Secondary analyses by estimated CVD risk strata showed modestly improved risk discrimination with CAC also among low- and high-estimated risk groups. CONCLUSIONS: Our findings support the current guideline recommendation to use, among available risk scores, the PCE for initial risk assessment and to use CAC for further risk assessment in a broad borderline and intermediate risk group. Also, in select individuals at low or high estimated risk, CAC modestly improved discrimination. Studies in unselected populations will lead to further understanding of the potential value of tools combining risk scores and CAC for optimal risk assessment.
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