Development and Validation of a Multimodal Nomogram for Carotid Vulnerable Plaques Based on Ultrasound Features,...

This study aimed to develop and validate a multimodal nomogram that integrates ultrasound features, clinical risk factors, and systemic inflammatory markers to predict carotid vulnerable plaques (VPs), assess its precision in recognizing high-risk plaques associated with stroke, and offer personalized clinical interventions.

A total of 146 patients who underwent carotid endarterectomy (CEA) between April 2024 and May 2025 were enrolled in this prospective study and subsequently divided into a training cohort (n = 100) and a validation cohort (n = 46). The candidate variables comprised clinical characteristics, laboratory parameters, and multimodal ultrasound plaque features. The least absolute shrinkage and selection operator (LASSO) regression was utilized for robust variable selection to mitigate overfitting. A nomogram was developed based on the selected features, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC).

The LASSO regression analysis identified 8 independent predictors for the nomogram: neutrophil-to-lymphocyte ratio (NLR), intraplaque neovascularization (IPN) grade, smoking history, neutrophil count, body mass index (BMI), plaque echogenicity, C-reactive protein (CRP), and plaque morphology. The nomogram exhibited excellent discriminative capacity, with an AUC of 0.911 (95% CI: 0.851-0.972) in the training cohort and 0.889 (95% CI: 0.786-0.992) in the validation cohort. Calibration curves demonstrated good concordance between predictions and observations, whereas DCA and CIC confirmed the model's favorable clinical utility.

The developed multimodal nomogram accurately predicts carotid VPs, facilitates patient risk stratification, and supports the initiation of targeted preventive therapies for those at the highest risk.