Predicting vessel recanalization in extracranial internal carotid artery dissection: a nomogram based on ultrasonography and clinical features

Abstract

Background: Extracranial internal carotid artery dissection (EICAD) is a prominent factor in ischemic stroke in young patients, and vessel recanalization is correlated with stroke recurrence. We propose to determine the possible association between carotid duplex ultrasound (CDU) features, clinical factors, and vessel recanalization in EICAD patients. Methods: In the current retrospective study, data from 202 patients diagnosed with EICAD by CDU and confirmed by computed tomography angiography (CTA) or high-resolution magnetic resonance imaging (HRMRI) were encompassed. Patients were randomized 7:3 into training cohort (n = 142) and validation cohort (n = 60). The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were used to build a nomogram to predict recanalization. At last, we assessed the performance of the nomogram with an area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: The nomogram included CDU features (intramural hematoma, Intraluminal thrombus, and stenosis degree) and age, with AUC values of 0.906 (95% CI: 0.857–0.946) and 0.903 (95% CI: 0.820–0.963) in the training cohort and the validation cohort, respectively. Using a probability cutoff of 0.5 derived from the Youden index, patients were stratified into high-risk (recanalization probability <50%) and low-risk groups (≥50%). DCA showed that the nomogram performed significantly better across various threshold probabilities, and CIC demonstrated that the nomogram offers superior net benefit across a broad range of threshold probabilities, indicating its significant predictive value. Conclusion: A nomogram depended on CDU and clinical features could accurately predict recanalization in EICAD patients. The nomogram may facilitate early identification of high-risk patients and personalized therapeutic strategies.