Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease (Jun 2018)
Aortic Arch Width and Cardiovascular Disease in Men and Women in the Community
Abstract
Background We sought to determine whether increased aortic arch width (AAW) adds to standard Framingham risk factors and coronary artery calcium (CAC) for prediction of incident adverse cardiovascular disease (CVD) events in community‐dwelling adults. Methods and Results A total of 3026 Framingham Heart Study Offspring and Third Generation cohort participants underwent noncontrast multidetector computed tomography from 2002 to 2005 to quantify CAC. We measured AAW as the distance between the centroids of the ascending and descending thoracic aorta, at the level of main pulmonary artery bifurcation or the right pulmonary artery. We determined sex, age group, and body size specific cut points for high (≥90th percentile) AAW from a healthy referent group (N=1471) and dichotomized AAW as high or not high across all study participants. Clinical covariates were obtained at Offspring cycle 7 (1998–2001) or Third Generation cycle 1 (2002–2005) examinations. The primary CVD outcome was a composite of myocardial infarction, coronary insufficiency, cerebrovascular accident, first hospitalization for heart failure, or CVD death. Cox proportional hazards models were used to estimate hazard ratio of high AAW on time‐to‐incident CVD after adjustment for Framingham risk factors and CAC. Net reclassification improvement was used to assess the effect of adding AAW to the baseline Framingham risk factor+CAC model. A total of 2826 participants (aged 51±11 years, 48% women) had complete covariates and were free of CVD at multidetector computed tomography. Over a median 8.9 years of follow‐up, there were 135 incident CVD events. High AAW was independently predictive of CVD events (hazard ratio, 1.55; P=0.032) and appropriately reclassified participants at risk: net reclassification improvement, 0.31 (95% confidence interval, 0.15–0.48). Conclusion AAW augments traditional CVD risk factors and CAC for prediction of incident adverse CVD events among community‐dwelling adults.
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