Journal of clinical ultrasound : JCU · 2025
Development and Validation of a Prediction Model for Acute Ischemic Stroke Based on a Novel Ultrasound-Based Carotid Plaque Scoring System and Clinical Factors.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Sun Xiaowen, Niu Huimin, Pei Yueying, Wang Xiaona, Chen Min
Аннотация
BACKGROUND: Acute ischemic stroke (AIS) is characterized by high incidence, sudden onset, and often poor prognosis. Carotid atherosclerosis plays a crucial role in its pathogenesis, and ultrasound imaging offers a non-invasive method for evaluating carotid plaque characteristics. This study aimed to develop and validate a prediction model for AIS risk based on a novel ultrasound-based carotid plaque scoring system combined with clinical risk factors.
METHODS: A retrospective case-control study was conducted, including 100 patients with carotid atherosclerosis who attended Hebei General Hospital from January 2022 to December 2023. Patients were divided into an AIS group (n = 60) and a non-AIS group (n = 40). Univariate and multivariable logistic regression analyses were used to identify significant ultrasound and clinical predictors of AIS. A carotid plaque ultrasound scoring system was constructed based on selected imaging features and integrated with clinical risk factors to establish a nomogram prediction model for AIS. The performance of the scoring system and the combined model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration plots. Internal validation of the AIS prediction model was performed using the bootstrap resampling method.
RESULTS: Independent predictors of AIS included plaque echogenicity, morphology, contrast-enhanced ultrasound score, history of hypertension, C-reactive protein (CRP), fasting blood glucose (FBG), and very-low-density lipoprotein (VLDL). The ultrasound scoring system alone yielded an area under the curve (AUC) of 0.88, while the integrated model incorporating clinical factors achieved an AUC of 0.94. Both calibration and decision curves demonstrated excellent agreement and clinical utility. Bootstrap validation confirmed the model's robustness and reliability.
CONCLUSIONS: The carotid plaque ultrasound scoring system provides a reliable method for AIS risk prediction. The AIS prediction model, developed using this scoring system combined with clinical risk factors, improves predictive accuracy and helps in the early identification of high-risk patients.
METHODS: A retrospective case-control study was conducted, including 100 patients with carotid atherosclerosis who attended Hebei General Hospital from January 2022 to December 2023. Patients were divided into an AIS group (n = 60) and a non-AIS group (n = 40). Univariate and multivariable logistic regression analyses were used to identify significant ultrasound and clinical predictors of AIS. A carotid plaque ultrasound scoring system was constructed based on selected imaging features and integrated with clinical risk factors to establish a nomogram prediction model for AIS. The performance of the scoring system and the combined model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration plots. Internal validation of the AIS prediction model was performed using the bootstrap resampling method.
RESULTS: Independent predictors of AIS included plaque echogenicity, morphology, contrast-enhanced ultrasound score, history of hypertension, C-reactive protein (CRP), fasting blood glucose (FBG), and very-low-density lipoprotein (VLDL). The ultrasound scoring system alone yielded an area under the curve (AUC) of 0.88, while the integrated model incorporating clinical factors achieved an AUC of 0.94. Both calibration and decision curves demonstrated excellent agreement and clinical utility. Bootstrap validation confirmed the model's robustness and reliability.
CONCLUSIONS: The carotid plaque ultrasound scoring system provides a reliable method for AIS risk prediction. The AIS prediction model, developed using this scoring system combined with clinical risk factors, improves predictive accuracy and helps in the early identification of high-risk patients.