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Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study: what have we learned from it?

The Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study is the largest natural history study on patients with 50–99% asymptomatic carotid stenosis (ACS). It included 1,121 ACS individuals with a follow-up between 6 and 96 months (mean: 48 months). During the last 15 years, several importa...

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Detalles Bibliográficos
Autores principales: Paraskevas, Kosmas I., Nicolaides, Andrew N., Kakkos, Stavros K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7607063/
https://www.ncbi.nlm.nih.gov/pubmed/33178803
http://dx.doi.org/10.21037/atm.2020.02.156
Descripción
Sumario:The Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study is the largest natural history study on patients with 50–99% asymptomatic carotid stenosis (ACS). It included 1,121 ACS individuals with a follow-up between 6 and 96 months (mean: 48 months). During the last 15 years, several important ACSRS substudies have been published that have contributed significantly to the optimal management of ACS patients. These studies have demonstrated that specific baseline clinical characteristics and ultrasonic plaque features after image normalization (namely carotid plaque type, gray scale median, carotid plaque area, juxtaluminal black area without a visible echogenic cup, discrete white areas in an echolucent part of a plaque, silent embolic infarcts on brain computed tomography scans, a history of contralateral transient ischemic attacks/strokes) can independently predict future ipsilateral cerebrovascular events. The ACSRS study provided proof that by use of a computer program to normalize plaque images and extract plaque texture features, a combination of features can stratify patients into various categories depending on their stroke risk. The present review will discuss the various reported predictors of future ipsilateral cerebrovascular events and how these characteristics can be used to calculate individual stroke risk.