Key result
Multimodal nomogram integrating ultrasound, clinical, and inflammatory markers predicts carotid vulnerable plaques with ~0.89 AUC.
Why the study?
To develop and validate a multimodal nomogram combining ultrasound features, clinical risk factors, and systemic inflammatory markers to predict carotid vulnerable plaques associated with stroke.
Does a multimodal nomogram integrating ultrasound, clinical, and inflammatory markers accurately predict carotid vulnerable plaques in patients undergoing CEA?
Cohort (n=146)
Does a multimodal nomogram integrating ultrasound, clinical, and inflammatory markers accurately predict carotid vulnerable plaques in patients undergoing CEA?
A novel multimodal nomogram incorporating ultrasound, clinical, and inflammatory markers demonstrated excellent discriminative capacity for predicting carotid vulnerable plaques.
May improve preoperative risk stratification in CEA candidates; hypothesis-generating and requires prospective validation.
OBJECTIVES: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.
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Liu et al. (2025) conducted a cohort in Carotid vulnerable plaques (n=146). Multimodal nomogram was evaluated on Prediction of carotid vulnerable plaques (AUC) (AUC, 95% CI 0.786-0.992). A multimodal nomogram integrating ultrasound, clinical, and inflammatory markers accurately predicted carotid vulnerable plaques, achieving an AUC of 0.889 (95% CI 0.786-0.992) in the validation cohort.
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