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Clinical-sonographic index (CSI): a novel transcranial Doppler diagnostic model for middle cerebral artery stenosis

Cited 2 time in Web of Science Cited 2 time in Scopus
Authors
Jung, Keun-Hwa; Lee, Yong-Seok
Issue Date
2008-02-29
Publisher
Wiley-Blackwell
Citation
J Neuroimaging. 2008;18(3):256-261
Keywords
Arterial Occlusive Diseases/physiopathology/*ultrasonographyBlood Flow VelocityCase-Control StudiesCerebral Arterial Diseases/physiopathology/*ultrasonographyChi-Square DistributionFemaleHumansLogistic ModelsMagnetic Resonance AngiographyMaleMiddle AgedMiddle Cerebral Artery/physiopathology/*ultrasonographyROC CurveRisk Factors*Ultrasonography, Doppler, Transcranial
Abstract
BACKGROUND: Transcranial Doppler sonography is useful for the diagnosis of middle cerebral artery (MCA) stenosis. Although the previous studies have focused on the elevated mean flow velocity (MFV) or asymmetry of MFV, the lack of clinical correlation might limit diagnostic accuracy. We try to develop and validate a new diagnostic model including more comprehensive clinical and sonographic parameters. METHODS: Consecutive patients with magnetic resonance angiography (MRA)-verified MCA stenosis were included, and compared with control subjects with normal MCA. The age, sex, corresponding symptoms (CS) to sonographic side, diabetes mellitus (DM), and hypertension were included for analysis. As sonographic parameters, MFV (cm/sec), asymmetry index (AI,%), and difference of pulsatility index (DeltaPI) were analyzed. Clinical-sonographic index (CSI) model was built with significant parameters by multivariate logistic regression analysis. RESULTS: One hundred and seven patients (M:F = 53:54, age: 61.6 +/- 11.6 years), and 100 control subjects (M:F = 49:51, age: 54.9 +/- 14.5 years) were included. In logistic regression, MFV (odds ratio [OR], 1.057; 95% confidence interval [95% CI], 1.030-1.084), AI (OR, 1.067; 95% CI, 1.031-1.104), DeltaPI (OR, 41.754; 95% CI, 2.771-626.999), CS (OR, 15.904; 95% CI, 5.055-50.042), and DM (OR, 3.949; 95% CI, 1.132-13.783) were independent predictors of MCA stenosis. CSI was simplified for clinical use, CSI = MFV(cm/sec) + 3 * AI (%) + 180 *DeltaPI + 90 * CS(presence = 1, absence = 0) + 30 * DM (presence = 1, absence = 0). The area under the receiver operator characteristic (ROC) curve of MCA stenosis versus MFV, DeltaPI, AI, and CSI was .641, .668, .865 and .953. According to ROC curve, cut-off point for MCA stenosis was suggested as CSI > 180 (sensitivity: 87%, specificity: 92%). CONCLUSION: CSI might be useful to enhance diagnostic accuracy.
ISSN
1552-6569 (Electronic)
Language
English
URI
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18304037

http://hdl.handle.net/10371/68382
DOI
https://doi.org/10.1111/j.1552-6569.2007.00181.x
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College of Medicine/School of Medicine (의과대학/대학원)Dept. of Neurology (신경과학교실)Journal Papers (저널논문_신경과학교실)
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