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椎基底动脉系统的形态学特征预测自发性椎动脉夹层患者的缺血性卒中风险

Morphological Features of the Vertebrobasilar System Predict Ischemic Stroke Risk in Spontaneous Vertebral Artery Dissection.

作者信息

Bao Jiajia, Bai Mateng, Zhou Muke, Fang Jinghuan, Li Yanbo, Guo Jian, He Li

机构信息

Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.

Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Beijing, China.

出版信息

J Cardiovasc Transl Res. 2024 Dec;17(6):1365-1376. doi: 10.1007/s12265-024-10534-6. Epub 2024 Jul 9.

Abstract

The vertebral artery's morphological characteristics are crucial in spontaneous vertebral artery dissection (sVAD). We aimed to investigate morphologic features related to ischemic stroke (IS) and develop a novel prediction model. Out of 126 patients, 93 were finally analyzed. We constructed 3D models and morphological analyses. Patients were randomly classified into training and validation cohorts (3:1 ratio). Variables selected by LASSO - including five morphological features and five clinical characteristics - were used to develop prediction model in the training cohort. The model exhibited a high area under the curve (AUC) of 0.944 (95%CI, 0.862-0.984), with internal validation confirming its consistency (AUC = 0.818, 95%CI, 0.597-0.948). Decision curve analysis (DCA) indicated clinical usefulness. Morphological features significantly contribute to risk stratification in sVAD patients. Our novel developed model, combining interdisciplinary parameters, is clinically useful for predicting IS risk. Further validation and in-depth research into the hemodynamics related to sVAD are necessary.

摘要

椎动脉的形态学特征在自发性椎动脉夹层(sVAD)中至关重要。我们旨在研究与缺血性卒中(IS)相关的形态学特征,并开发一种新的预测模型。在126例患者中,最终对93例进行了分析。我们构建了三维模型并进行形态学分析。患者被随机分为训练组和验证组(比例为3:1)。通过LASSO选择的变量——包括五个形态学特征和五个临床特征——用于在训练组中开发预测模型。该模型的曲线下面积(AUC)高达0.944(95%CI,0.862 - 0.984),内部验证证实了其一致性(AUC = 0.818,95%CI,0.597 - 0.948)。决策曲线分析(DCA)表明了其临床实用性。形态学特征对sVAD患者的风险分层有显著贡献。我们新开发的结合多学科参数的模型在临床上可用于预测IS风险。有必要对与sVAD相关的血流动力学进行进一步验证和深入研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb8f/11634921/a947d7c51420/12265_2024_10534_Fig1_HTML.jpg

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