Carotid and femoral bifurcation plaques detected by ultrasound as predictors of cardiovascular events.
Authors
Affiliations (10)
Affiliations (10)
- Department of Vascular Diseases, University Medical Centre Ljubljana, Slovenia.
- Department of Internal Medicine, Faculty of Medicine, University of Ljubljana, Slovenia.
- Department of Basic and Clinical Science, University of Nicosia Medical School, Cyprus.
- Vascular Screening and Diagnostic Centre, Nicosia, Cyprus.
- Department of Vascular Surgery, Red Cross Hospital, Athens, Greece.
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK.
- Vascular Department, Surrey and Sussex Healthcare NHS Trust, East Surrey Hospital, Redhill, UK.
- Department of Internal Medicine III, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
- Department of Cardiology and Vascular Medicine, West German Heart and Vascular Centre, University of Duisburg-Essen, Duisburg, Germany.
- Department of Internal Medicine, Metabolic Diseases and Angiology, Faculty of Health Sciences in Katowice, Medical University of Silesia, Katowice, Poland.
Abstract
<b></b>Risk factor-based algorithms give a good estimate of cardiovascular (CV) risk at the population level but are often inaccurate at the individual level. Detecting preclinical atherosclerotic plaques in the carotid and common femoral arterial bifurcations by ultrasound is a simple, non-invasive way of detecting atherosclerosis in the individual and thus more accurately estimating his/her risk of future CV events. The presence of plaques in these bifurcations is independently associated with increased risk of CV death and myocardial infarction, even after adjusting for traditional risk factors, while ultrasonographic characteristics of vulnerable plaque are mostly associated with increased risk for ipsilateral ischaemic stroke. The predictive value of carotid and femoral plaques for CV events increases in proportion to plaque burden and especially by plaque progression over time. Assessing the burden of carotid and/or common femoral bifurcation plaques enables reclassification of a significant number of individuals with low risk according risk factor-based algorithms into intermediate or high CV risk and intermediate risk individuals into the low- or high CV risk. Ongoing multimodality imaging studies, supplemented by clinical and genetic data, aided by machine learning/ artificial intelligence analysis are expected to advance our understanding of atherosclerosis progression from the asymptomatic into the symptomatic phase and personalize prevention.