Use of carotid web angioarchitecture in stratification of stroke risk.

Abstract

Objective: To validate the carotid web (CW) risk stratification assessment described in previous works within a larger cohort of patients with symptomatic and incidentally found asymptomatic CWs.

Methods: A retrospective analysis of our institution's electronic medical records identified all patients with a diagnosis of CW from 2017 to 2024. We included symptomatic patients and those with asymptomatic CWs, that is, incidentally found webs without history of stroke or transient ischemic attack. Patient charts were reviewed for demographics, imaging, comorbidities, and a diagnosis of stroke after diagnosis of asymptomatic CW. All angles were measured as described in previous work on a sagittal reconstruction of neck CT angiography in which the common carotid artery (CCA), external carotid artery, and internal carotid artery (ICA) were well visualized, together with the CW itself. Principal component analysis and logistic regression were performed to evaluate the association between high-risk angles and stroke risk.  RESULTS: Twenty-six symptomatic and 26 asymptomatic patients were identified. Of note, the number of patients with hypertension, hyperlipidemia, and smoking history was 17 (65.0%), 16 (62.0%), and 8 (31.0%) for symptomatic patients and 18 (69.0%), 17 (65.0%), and 15 (58.0%) for asymptomatic patients. All angular measurements showed statistically significant associations with stroke status. The CCA-web-pouch angle showed the strongest association (p=2.07×10⁻⁴), followed by the CCA-pouch-tip angle (p=3.23×10⁻⁴), ICA-web-pouch angle (p=0.004), and ICA-pouch-tip angle (p=0.005). Each additional high-risk angle increased the odds of stroke by 9.47-fold (p<0.0001). The associated probability of stroke increased from 6.3% with no high-risk angles to 39.1% with one high-risk angle and further to 85.9% with two high-risk angles. The model demonstrated high sensitivity, correctly identifying 84.6% of positive cases, and high specificity, correctly identifying 88.5% of negative cases. The F1 score was 0.863, indicating good overall model performance.  CONCLUSION: Given this successful stratification of CWs into high- and low-risk groups, the utilization of geometric CW parameters may play a role in improving patient selection for intervention in the setting of incidentally diagnosed CW. .