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Tison, Geoffrey H., MD, MPH; Guo, Mengye, PhD; Blaha, Michael J., MD, MPH; McClelland, Robyn L., PhD; Allison, Matthew A., MD, MPH; Szklo, Moyses, MD, DrPH; Wong, Nathan D., PhD, MPH; Blumenthal, Roger S., MD; Budoff, Matthew J., MD; Nasir, Khurram, MD, MPH
Journal of cardiovascular computed tomography, 09/2015, Volume: 9, Issue: 5Journal Article
Abstract Background Cardiovascular calcification outside of the coronary tree, known as extracoronary calcification (ECC), is highly prevalent, often occurs concurrently in multiple sites, and yet its prognostic value is unclear. Objective To determine whether multisite ECC is associated with coronary heart disease (CHD) events, CHD mortality, and all-cause mortality. Methods We evaluated 5903 participants from the Multi-Ethnic Study of Atherosclerosis without diabetes who underwent CT imaging for calcification of the aortic valve, aortic root, mitral valve, and thoracic aorta. Participants were followed for 10.3 years. Multivariable adjusted hazard ratios estimated risk of outcomes for increasing numbers of ECC sites (0, 1, 2, 3, and 4), and receiver operator characteristic analysis assessed model discrimination. Results Prevalence of any ECC was 45%; median age was 62 years. Compared with those without ECC, those with ECC in 4 sites had increased hazards of 4.5, 7.1 and 2.3 for CHD events, CHD mortality, and all-cause mortality, respectively, independent of traditional risk factors (TRF; all P ≤ .05), and had ≥2-fold increased hazards for outcomes independent of coronary artery calcification (CAC). Each additional site of ECC was positively associated with each outcome in a graded fashion. When added to TRF, ECC significantly increased the area under the receiver operator characteristic curve for all outcomes and modestly increased the area under the curve for mortality beyond TRF + CAC (0.799 to 0.802; P = .03). Conclusion Increasing multisite ECC has a graded association with higher CHD and mortality risk, contributing information beyond TRF. Multisite ECC incidentally identified on imaging can be used to improve individualized risk prediction.
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