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.

椎动脉的形态特征对于自发性椎动脉夹层（sVAD）至关重要。我们的目的是研究与缺血性中风（IS）相关的形态学特征并开发一种新的预测模型。最终对 126 名患者中的 93 名进行了分析。我们构建了 3D 模型并进行形态分析。患者被随机分为训练组和验证组（比例为 3:1）。 LASSO 选择的变量（包括五种形态特征和五种临床特征）用于开发训练队列中的预测模型。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）表明其临床有用性。形态学特征对 sVAD 患者的风险分层有显着影响。我们新开发的模型结合了跨学科参数，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。