症状性颅内动脉中重度狭窄脑梗死患者早期神经功能恶化预测模型的构建与验证

doi:10.3969/j.issn.1673-5765.2025.12.005

摘要/Abstract

摘要： 目的分析症状性颅内动脉中重度狭窄脑梗死患者早期神经功能恶化（early neurological deterioration，END）的危险因素，基于独立危险因素构建并验证END预测模型。
方法本研究为回顾性研究，连续纳入2019年1月—2023年12月广东省中医院脑血管病科收治的符合纳排标准的症状性颅内动脉中重度狭窄脑梗死患者，根据发病7 d内是否发生END分为END组和非END组。通过单因素及多因素logistic回归分析筛选END的独立危险因素。随后，基于所得独立危险因素构建列线图预测模型，采用Bootstrap重采样法在原始数据集中进行有效性验证。
结果共纳入152例患者，其中END组47例，非END组105例，END组患者年龄为65（57～75）岁，男性33例（70.21%）；非END组患者年龄为64（54～75）岁，男性72例（68.57%）。单因素及多因素logistic回归分析显示，急性分水岭梗死、责任血管重度狭窄及较高的年龄、血压、临床特征、症状持续时间、糖尿病、双重TIA、同侧颈动脉狭窄和DWI梗死灶（age，blood pressure，clinical features，duration of symptoms，diabetes，dual transient ischemic attack，ipsilateral carotid stenosis，infarction on
diffusion-weighted-imaging；ABCD3-I）评分是END的独立危险因素。基于上述危险因素构建列线图预测模型，其AUC为0.85（95%CI 0.78～0.92），最佳截断值为0.43，此时模型的敏感度为0.77，特异度为0.79。采用Bootstrap重采样法对模型进行内部验证，AUC为0.85（95%CI 0.84～0.89）。
结论本研究构建的END预测模型包含急性分水岭梗死、责任血管重度狭窄及ABCD3-I评分，具有良好的预测效能，内部验证表明模型稳定性良好。

文章导读： 本研究针对症状性颅内动脉中重度狭窄脑梗死患者，构建并验证了一个融合影像学特征与临床评分的早期神经功能恶化预测模型，具有较高的预测效能与良好的临床转化潜力。

关键词: 症状性颅内动脉狭窄; 早期神经功能恶化; 预测模型

Abstract: Objective To analyze the risk factors for early neurological deterioration (END) in patients with cerebral infarction caused by moderate-to-severe symptomatic intracranial arterial stenosis, and to develop and validate a prediction model for END based on independent risk factors.
Methods This was a retrospective study that consecutively enrolled patients with cerebral infarction caused by moderate-to-severe symptomatic intracranial arterial stenosis, who were admitted to the Department of Cerebrovascular Disease, Guangdong Provincial Hospital of Chinese Medicine between January 2019 and December 2023. Patients were divided into the END group and the non-END group based on the occurrence of END within 7 days of onset. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for END. A nomogram regression prediction model was subsequently constructed based on these factors, and its validity was verified using the Bootstrap resampling method within the original dataset.
Results A total of 152 patients were included, with 47 in the END group and 105 in the non-END group. The END group consisted of 47 patients aged 65 (57-75) years, including 33 males (70.21%); the non-END group consisted of 105 patients aged 64 (54-75) years, including 72 males (68.57%). Univariate and multivariate logistic regression analyses identified acute cerebral watershed infarction, severe stenosis of the responsible vessel, and a higher age, blood pressure, clinical features, duration of symptoms, diabetes, dual transient ischemic attack, ipsilateral carotid stenosis, infarction on diffusion-weighted-imaging (ABCD3-I) score as independent risk factors for END. A nomogram regression prediction model was constructed based on these three factors, with an AUC of 0.85 (95%CI 0.78-0.92) and an optimal cut-off value of 0.43. The sensitivity and specificity were 0.77 and 0.79, respectively. Internal validation of the model was performed using the Bootstrap resampling method, yielding an AUC of 0.85 (95%CI 0.84-0.89).
Conclusions The END prediction model constructed in this study incorporates acute cerebral watershed infarction, severe stenosis of the responsible vessel, and the ABCD3-I score, demonstrating good predictive performance. Internal validation indicates good model stability.

Key words: Symptomatic intracranial arterial stenosis; Early neurological deterioration; Prediction model

中图分类号:

熊紫妮, 彭株丽, 王欣迪, 刘浩林, 陈小龙, 白小欣. 症状性颅内动脉中重度狭窄脑梗死患者早期神经功能恶化预测模型的构建与验证[J]. 中国卒中杂志, 2025, 20(12): 1499-1507.

XIONG Zini, PENG Zhuli, WANG Xindi, LIU Haolin, CHEN Xiaolong, BAI Xiaoxin. Development and Validation of an Early Neurological Deterioration Prediction Model for Patients with Cerebral Infarction Caused by Moderate-to-Severe Symptomatic Intracranial Arterial Stenosis[J]. Chinese Journal of Stroke, 2025, 20(12): 1499-1507.

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