Carotid artery echolucency, texture features, and incident cardiovascular disease events: The mesa study

Background-—We hypothesized that measures of common carotid artery echolucency and grayscale texture features were associated with cardiovascular disease (CVD) risk factors and could predict CVD events. Methods and Results-—Using a case-cohort design, we measured common carotid artery ultrasound images from 1788 participants in Exam 1 of the MESA study (Multi-Ethnic Study of Atherosclerosis) to derive 4 grayscale features: grayscale median, entropy, gray level difference statistic-contrast, and spatial gray level dependence matrices-angular second moment. CVD risk factor associations were determined by linear regression. Cox proportional hazard models with inverse selection probability weighting and adjustments for age, sex, race/ethnicity, CVD risk factors, and C-reactive protein were used to determine if standardized values for grayscale median, entropy, gray level difference statistic-contrast, and spatial gray level dependence matrices-angular second moment could predict incident coronary heart disease, stroke, and total CVD events over a median 13 years follow-up. Participants were mean (SD) 63.1 (10.3) years of age, 52.6% female, 32.1% white, 27.8% black, 23.3% Hispanic, and 16.8% Chinese. There were 283 coronary heart disease, 120 stroke, and 416 CVD events. Several associations of grayscale features with CVD risk factors were identified. In fully adjusted models, higher gray level difference statistic-contrast was associated with a lower risk of incident coronary heart disease (hazard ratio 0.82, 95% CI 0.71–0.94, p_adj=0.005) and CVD events (hazard ratio 0.87, 95% CI 0.77–0.98, p_adj=0.018); higher spatial gray level dependence matrices-angular second moment was associated with a higher risk of CVD events (hazard ratio 1.09, 95% CI 1.00–1.19, p_adj=0.044). Conclusions-—Gray level difference statistic-contrast and spatial gray level dependence matrices-angular second moment predicted CVD events independent of risk factors, indicating their potential use as biomarkers to assess future CVD risk.