Saratov JOURNAL of Medical and Scientific Research

Vozdeeva A.S.

Saratov State Medical University n.a. VI. Razumovsky Student

Carotid arteries ultrasound screening in asymptomatic patients

Year: 2019, volume 15 Issue: №1 Pages: 190-199
Heading: Neurology Article type: Original article
Authors: Shchanitsyn I.N., Shvarts E.Yu., Ishmukhametova R.A., Vozdeeva A.S.
Organization: Saratov State Medical University
Summary:

Objective: to reveal the most important predictors of carotid artery stenosis in ambulatory patients of Saratov and Saratov Region with the help of multivariate analysis and to define an ideal patient for ultrasound screening. Material and Methods. In 2014-2018 off-site consultations and ultrasound screening were performed for asymptomatic patients with suspected carotid artery disease in outpatient clinics of Saratov and Saratov Region. Those patients were referred for screening by neurologists and general practitioners. The study used 470 medical charts. The multivariate regression analysis was performed to identify independent predictors of carotid artery stenosis (>50%). Results. The >30% carotid artery stenosis was detected in 24.5% (115/470) of participants, >50% stenosis — in 10.2% (48/470) of participants, >70% stenosis — in 2.9% (14/470) of participants. The multivariate analysis revealed that the odds of finding >50% stenosis in patients chosen by neurologists and general physicians for screening was significantly higher in the presence of following factors: age older then 72, male sex, acute cerebrovascular event more than 6 months ago, atherosclerosis in arteries of lower extremities, episodes of speech disorder. The point scale for risk assessment has been created. In the absence of prognostic factors the absolute risk of >50% stenosis was only 3%. In the presence of 1 point it was 16% and more than 50% in the presence of 4 points. Conclusion. The analysis we conducted allowed to specify major predictors of atherosclerosis in patients followed in outpatient clinics of Saratov and Saratov Region and to create the patient model which allows to optimize selection for ultrasound screening.

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