Abstract: BACKGROUND: Studies have shown that the vulnerability and elasticity of carotid plaques are related to the presence and degree of neovascularization within the plaque. Ultrasound, as the preferred measure to screen and evaluate vulnerable carotid plaques, is non-invasive, easy to perform, highly reproducible and radiation-free.
OBJECTIVE: To investigate the influencing factors of vulnerable carotid plaque in the high-risk stroke population based on clinical baseline characteristics and carotid ultrasound parameters, and to develop a Nomogram prediction model based on independent risk factors. 
METHODS: A total of 180 patients who were identified to be at high risk of stroke by stroke screening at Fuzhou First People’s Hospital from November 2021 to November 2023 were retrospectively selected as the study objects, and the patients were divided into a modeling set (n=126) and a validation set (n=54) at a ratio of 7:3. According to the results of carotid artery ultrasound, the subjects in the modeling set were divided into a vulnerable plaque group (n=54) and a non-vulnerable plaque group (n=72). Independent risk factors were obtained by multi-factor Logistic regression, and a Nomogram model was constructed. Decision curves were drawn using R language to evaluate the clinical benefit of the model. The predictive efficacy of the model was tested by receiver operating characteristic curve and calibration curve, and the case data of the validation set were analyzed for external validation. 
RESULTS AND CONCLUSION: Multivariate Logistic regression results showed that age, family history of stroke, maximum carotid plaque thickness, carotid plaque quantity, urine microalbumin, urine microalbumin/creatinine ratio were associated with vulnerable carotid plaques in patients at high risk of stroke (P < 0.05). The area under curve of the established Nomogram model was 0.917, and the sensitivity and specificity were 79.6% and 91.7%, respectively. The results of decision curve showed that the potential clinical benefit of this model was considerable and its usability was high. The calibration curve results showed that the model had good prediction accuracy. The verification set results showed that the external prediction performance of the model was good. To conclude, age, family history of stroke, and maximum carotid plaque thickness in the high-risk population are all factors that influence this prediction model. This Nomogram based on these independent risk factors can provide a powerful reference for the clinical treatment of this high-risk population.
中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

Key words: clinical baseline characteristics, carotid ultrasound parameters, high-risk stroke population, carotid plaque, Nomogram