JOURNAL ARTICLE
MULTICENTER STUDY

Carotid artery plaque morphology and composition in relation to incident cardiovascular events: the Multi-Ethnic Study of Atherosclerosis (MESA)

Anna E H Zavodni, Bruce A Wasserman, Robyn L McClelland, Antoinette S Gomes, Aaron R Folsom, Joseph F Polak, João A C Lima, David A Bluemke
Radiology 2014, 271 (2): 381-9
24592924

PURPOSE: To determine if carotid plaque morphology and composition with magnetic resonance (MR) imaging can be used to identify asymptomatic subjects at risk for cardiovascular events.

MATERIALS AND METHODS: Institutional review boards at each site approved the study, and all sites were Health Insurance Portability and Accountability Act (HIPAA) compliant. A total of 946 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) were evaluated with MR imaging and ultrasonography (US). MR imaging was used to define carotid plaque composition and remodeling index (wall area divided by the sum of wall area and lumen area), while US was used to assess carotid wall thickness. Incident cardiovascular events, including myocardial infarction, resuscitated cardiac arrest, angina, stroke, and death, were ascertained for an average of 5.5 years. Multivariable Cox proportional hazards models, C statistics, and net reclassification improvement (NRI) for event prediction were determined.

RESULTS: Cardiovascular events occurred in 59 (6%) of participants. Carotid IMT as well as MR imaging remodeling index, lipid core, and calcium in the internal carotid artery were significant predictors of events in univariate analysis (P < .001 for all). For traditional risk factors, the C statistic for event prediction was 0.696. For MR imaging remodeling index and lipid core, the C statistic was 0.734 and the NRI was 7.4% and 15.8% for participants with and those without cardiovascular events, respectively (P = .02). The NRI for US IMT in addition to traditional risk factors was not significant.

CONCLUSION: The identification of vulnerable plaque characteristics with MR imaging aids in cardiovascular disease prediction and improves the reclassification of baseline cardiovascular risk.

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