Neuroradiology · 2026
Morphological and clinical predictors of symptomatic carotid webs: a cross-validated analysis of 124 webs.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Mendoza-Ayus Santiago, Akkipeddi Sajal Medha, Gunn Kiernan, Schartz Derrek, Finkelstein Alan, Cotroneo Matthew, Mungara Tharan, Dhar Vidhya, Susa Stephen, Kohli Gurkirat, Nguyen Vincent, Mattingly Thomas, Bhalla Tarun, Bender Matthew
Аннотация
PURPOSE: Carotid webs (CWs) are an underrecognized cause of cryptogenic ischemic stroke and transient ischemic attack (TIA). Morphological characteristics are believed to influence their potential to become symptomatic. We aimed to develop and internally validate a reliable predictive model for symptomatic CWs.
METHODS: We conducted an institutional retrospective study including patients with CWs identified on Computed Tomography Angiography (CTA). Demographic, clinical, and morphological data were extracted. Symptomatic CWs were defined by a history of stroke or TIA without a more likely cause based on TOAST criteria. To ensure robust internal validation and mitigate overfitting, 5-fold cross-validation was employed. Univariable logistic regression was applied to all variables, followed by multivariable analysis including predictors with p < 0.15. Model performance was assessed using the cross-validated area under the receiver operating characteristic (ROC) curve (AUC).
RESULTS: A total of 124 CWs from 115 patients were identified, comprising 23 symptomatic and 101 asymptomatic webs (identified in 92 patients). The symptomatic group had higher rates of hypertension (91% vs. 59%, p = 0.003), hyperlipidemia (91% vs. 61%, p = 0.006), and CWs that were longer (3.0 mm [IQR 2.5-3.6] vs. 2.3 mm [IQR 2.0-3.0], p = 0.002) and thicker (1.5 mm [IQR 1.0-2.8] vs. 1.0 mm [IQR 0.9-1.5], p = 0.008). In the multivariable model, female sex (OR: 4.11, 95% CI: 1.31-15.3, p = 0.022), hypertension (OR: 7.58, 95% CI: 1.86-52.4, p = 0.013), and stenosis (OR: 1.06, 95% CI: 1.02-1.11, p = 0.003) were independent predictors of symptomatic status. The model yielded a 5-fold cross-validated AUC of 0.78, with a sensitivity of 91% and a specificity of 60% at the optimal probability threshold.
CONCLUSION: Female sex, hypertension, and local stenosis are independently associated with symptomatic CWs. This cross-validated model provides a framework for identifying high-risk carotid webs and may aid in clinical risk stratification.
METHODS: We conducted an institutional retrospective study including patients with CWs identified on Computed Tomography Angiography (CTA). Demographic, clinical, and morphological data were extracted. Symptomatic CWs were defined by a history of stroke or TIA without a more likely cause based on TOAST criteria. To ensure robust internal validation and mitigate overfitting, 5-fold cross-validation was employed. Univariable logistic regression was applied to all variables, followed by multivariable analysis including predictors with p < 0.15. Model performance was assessed using the cross-validated area under the receiver operating characteristic (ROC) curve (AUC).
RESULTS: A total of 124 CWs from 115 patients were identified, comprising 23 symptomatic and 101 asymptomatic webs (identified in 92 patients). The symptomatic group had higher rates of hypertension (91% vs. 59%, p = 0.003), hyperlipidemia (91% vs. 61%, p = 0.006), and CWs that were longer (3.0 mm [IQR 2.5-3.6] vs. 2.3 mm [IQR 2.0-3.0], p = 0.002) and thicker (1.5 mm [IQR 1.0-2.8] vs. 1.0 mm [IQR 0.9-1.5], p = 0.008). In the multivariable model, female sex (OR: 4.11, 95% CI: 1.31-15.3, p = 0.022), hypertension (OR: 7.58, 95% CI: 1.86-52.4, p = 0.013), and stenosis (OR: 1.06, 95% CI: 1.02-1.11, p = 0.003) were independent predictors of symptomatic status. The model yielded a 5-fold cross-validated AUC of 0.78, with a sensitivity of 91% and a specificity of 60% at the optimal probability threshold.
CONCLUSION: Female sex, hypertension, and local stenosis are independently associated with symptomatic CWs. This cross-validated model provides a framework for identifying high-risk carotid webs and may aid in clinical risk stratification.