Validation of two different grading schemes to identify patients with asymptomatic carotid artery stenosis in general population.
BACKGROUND
Identification of significant asymptomatic carotid artery stenosis (ACAS) is important because of potential stroke-risk reduction offered by carotid endarterectomy. We present an external validation of two previously developed scoring schemes designed to identify patients with ACAS.


METHODS
We used the data from the Cardiovascular Health Study (CHS)-a cohort study of cardiovascular risk factors, for external validation. Carotid Doppler ultrasound was performed in study participants. Two grading schemes, which used age more than 65 years, current smoking, and history of coronary artery disease and hyperlipidemia as predictors for ACAS, were validated using this dataset.


RESULTS
A total of 5,449 persons (mean age 72 +/- 5 years; 42% men; and 84% white) were screened. The overall prevalence of ACAS of > or =50% was 4.2%. The prevalence of ACAS in the highest risk category was 19% in both stratification schemes. The stratification remained effective in the white sub-population (P < .001), but was not significant in the African American population (P > .05).


CONCLUSIONS
Both schemes were effective in identifying persons with ACAS among general population aged 65 years or greater. A subset with a prevalence of ACAS of greater than 20% can be identified using these schemes making screening cost-effective among white population.
Suri
M Fareed K
MF
Zeenat Qureshi Stroke Research Center, Department of Neurology, University of Minnesota, Minneapolis, Minnesota 55455, USA. suri0027@umn.edu
Ezzeddine
Mustapha A
MA
Lakshminarayan
Kamakshi
K
Divani
Afshin A
AA
Qureshi
Adnan I
AI
D000368:Aged; D016893:Carotid Stenosis / Q000000981:diagnostic imaging* / Q000453:epidemiology; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D015995:Prevalence; D012372:ROC Curve; D018570:Risk Assessment; D018608:Ultrasonography, Doppler