Qiao Miao, Zhou Ting, Wang Rui, Jiang Yanhui, Liang Huitao, Meng Lingcui
Department of Ultrasound Imaging, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Aging Neurosci. 2025 Aug 18;17:1646916. doi: 10.3389/fnagi.2025.1646916. eCollection 2025.
Ischemic stroke (IS) is characterized by a high recurrence rate and more serious repercussions. Recently, the Carotid Plaque Reporting and Data System (Carotid Plaque-RADS) has been introduced to gauge and forecast the risk of cerebrovascular incidents. More studies are required to confirm its predictive power for recurrent ischemic stroke (RIS). We aimed to create a nomogram model that can evaluate the likelihood of RIS, with Carotid Plaque-RADS serving as a crucial instrument in this model.
We carried out a retrospective review of 286 patients diagnosed with acute IS at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2020 and January 2025. The study population consisted of two groups: the IS group (129 patients) and the RIS group (157 patients), depending on whether they experienced a recurrence of IS. Carotid ultrasound examination and clinical data were gathered and classified according to Carotid Plaque-RADS. Independent risk factors for the RIS were determined using multivariate logistic regression analyses. Subsequently, we developed a nomogram model to forecast RIS risk and evaluated its performance.
The RIS and IS groups showed significant differences in low-density lipoprotein (LDL), hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS categories. Multivariate logistic regression analysis identified LDL, hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS as independent risk factors for RIS. The nomogram model built using these risk factors demonstrated good calibration (H-L goodness-of-fit test = 0.354). Internal and external validation demonstrated that the calibration curves were consistent with the original curves. The nomogram model combining Carotid Plaque-RADS and clinical features showed area under the curve (AUC) values of 0.79 and 0.76, outperforming models using only clinical features (AUC 0.72 and 0.70) or only Carotid Plaque-RADS (AUC 0.71 and 0.69). The model showed considerable clinical benefit within the 0.2-0.8 threshold range in the decision curve analysis (DCA).
The nomogram model based on Carotid Plaque-RADS provides a novel and effective tool for clinical risk assessment and demonstrates favorable predictive performance for RIS.
缺血性卒中（IS）具有高复发率和更严重的后果。最近，颈动脉斑块报告与数据系统（Carotid Plaque - RADS）已被引入以评估和预测脑血管事件的风险。需要更多研究来证实其对复发性缺血性卒中（RIS）的预测能力。我们旨在创建一个列线图模型，该模型可以评估RIS的可能性，其中Carotid Plaque - RADS是该模型中的关键工具。
我们对2020年1月至2025年1月期间在广州中医药大学第二附属医院被诊断为急性IS的286例患者进行了回顾性研究。根据是否经历IS复发，研究人群分为两组：IS组（129例患者）和RIS组（157例患者）。收集颈动脉超声检查和临床数据，并根据Carotid Plaque - RADS进行分类。使用多因素逻辑回归分析确定RIS的独立危险因素。随后，我们开发了一个列线图模型来预测RIS风险并评估其性能。
RIS组和IS组在低密度脂蛋白（LDL）、高血压、心房颤动、严重颈动脉狭窄和Carotid Plaque - RADS类别方面存在显著差异。多因素逻辑回归分析确定LDL、高血压、心房颤动、严重颈动脉狭窄和Carotid Plaque - RADS为RIS的独立危险因素。使用这些危险因素构建的列线图模型显示出良好的校准（H - L拟合优度检验 = 0.354）。内部和外部验证表明校准曲线与原始曲线一致。结合Carotid Plaque - RADS和临床特征的列线图模型的曲线下面积（AUC）值分别为0.79和0.76，优于仅使用临床特征的模型（AUC 0.72和0.70）或仅使用Carotid Plaque - RADS的模型（AUC 0.71和0.69）。在决策曲线分析（DCA）中，该模型在0.2 - 0.8阈值范围内显示出相当大的临床益处。
基于Carotid Plaque - RADS的列线图模型为临床风险评估提供了一种新颖有效的工具，并对RIS表现出良好的预测性能。
