Utility of carotid duplex ultrasonography in a general inner-city hospital
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Updated: Apr 20, 2026

Point of Care Transcranial Color-Coded Duplex Ultrasound of the Middle Cerebral Artery
Published on: August 9, 2024
Mihir Barvalia1, David Silber, Michael DiVita
1Department of Medicine, Newark Beth Israel Medical Center, 201 Lyons Avenue at Osborne Terrace, Newark, NJ, USA. mihir.barvalia393@topper.wku.edu.
Background:
Carotid Duplex Ultrasonography (CDUS) is one of the non-invasive imaging modalities used to evaluate for carotid artery stenosis. However, it is often used in patients with coronary artery disease (CAD), peripheral artery disease (PAD), before heart surgery, syncope and non-specific neurological symptoms although its value is unclear. Our study aimed to further investigate the yield of CDUS in these conditions.
Methods:
A retrospective analysis was conducted on 827 consecutive carotid ultrasounds ordered between March 2013 and August 2013 at Newark Beth Israel Medical Center. Clinical characteristics such as age, sex, smoking status, systemic hypertension, diabetes mellitus, CAD, PAD, carotid bruit and indications for carotid ultrasound were included. Significant cerebrovascular disease (sCBVD) was defined as greater than or equal to 50% diameter reduction in internal carotid arteries (ICA) or any degree of occlusion in vertebrobasilar system.
Results:
Only 88 out of 827 (10.6%) patients had sCBVD. Using logistic regression analysis we identified age greater than 65 years (OR 2.1, 95% CI 1.2 to 3.7; P=0.006), carotid bruit (OR 7.8, 95% CI 3.6 to 16.6; P <0.001) and history of prior carotid endarterectomy or carotid artery stenting (OR 5.8, 95% CI 2.3 to 14.8; P <0.001) as significant predictors of sCBVD.
Conclusions:
Significant carotid artery stenosis is more likely in patients 65 years and older, presence of carotid bruit and prior CEA. On the other hand, it has low diagnostic yield in less than 65-year-old individuals, syncope and non-focal neurological symptoms. This highlights the need for better risk prediction models in order to promote optimal utilization.
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