首都医科大学学报 ›› 2026, Vol. 47 ›› Issue (3): 621-629.doi: 10.3969/j.issn.1006-7795.2026.03.026

• 临床研究 • 上一篇    下一篇

多发性腔隙性脑梗死后血管性帕金森综合征预测模型的建立及验证

高妍1,杨雪1,2,王华1,宁宇3,王玲玲1*   

  1. 1.北华大学附属医院神经内科，吉林 吉林  132011；2.敦化市医院神经内科，吉林 敦化  133700；3.北华大学附属医院普通外科，吉林 吉林 132011
  • 收稿日期:2025-12-22 修回日期:2026-03-06 出版日期:2026-06-21 发布日期:2026-06-26
  • 通讯作者: 王玲玲 E-mail:79225834@163.com
  • 基金资助:
    吉林省发展和改革委员会项目(2021C018),吉林省卫生与健康技术创新项目（2020J011）。

Establishment and validation of a predictive model for vascular Parkinsonism after multiple lacunar infarcts

Gao Yan1, Yang Xue1,2, Wang Hua1, Ning Yu3, Wang Lingling1*   

  1. 1. Department of Neurology, Affiliated Hospital of Beihua University, Jilin 132011,Jilin Province， China; 2. Department of Neurology, Dunhua City Hospital, Dunhua 133700,Jilin Province， China; 3. Department of General Surgery, Affiliated Hospital of Beihua University, Jilin 132011,Jilin Province，China
  • Received:2025-12-22 Revised:2026-03-06 Online:2026-06-21 Published:2026-06-26
  • Supported by:
    This study was supported by  Jilin Provincial Development and Reform Commission Project (2021C018)，Jilin Provincial Health and Technology Innovation Project (2020J011).

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摘要： 目的  探讨多发性腔隙性脑梗死（multi-lacunar infarction，MLI）患者继发血管性帕金森综合征（vascular Parkinsonism，VP）的相关危险因素，评估血液学指标与神经影像学特征在VP发生风险预测中的应用价值。方法  纳入2021年5月至2024年12月吉林省敦化市医院收治的MLI患者169例，根据是否继发VP分为MLI组和多发性腔隙性脑梗死后帕金森综合征（vascular Parkinsonism after multi-lacunar infarction, MLI-VP）组。采用随机分配方法，将患者按1∶1分为模型构建集（n=84）和内部评估集（n=85）。比较两组患者的临床资料及影像学特征，筛选MLI患者发生VP的危险因素，进而构建风险预测模型，并对其性能和临床应用价值进行评价。结果  中性粒细胞计数、高密度脂蛋白胆固醇（high density lipoprotein-cholesterol，HDL-C）、低密度脂蛋白胆固醇（low density lipoprotein-cholesterol，LDL-C）、基底节受累与MLI-VP 的发生显著相关（P<0.05）。中性粒细胞计数（OR=1.730，95%CI:1.046~3.105，P=0.047）、HDL-C降低（OR=0.166，95%CI:0.034~0.581，P=0.011）、LDL-C升高（OR=5.884，95%CI:1.520~31.075，P=0.019）和基底节受累（OR=3.941，95%CI:1.019~15.732，P=0.046）均为MLI-VP的独立危险因素。在此基础上构建列线图，模型受试者工作特征（receiver operating characteristic，ROC）曲线下面积为0.892（95%CI：0.806~0.892）。校准曲线显示拟合良好（Hosmer-Lemeshow P>0.05），决策曲线提示模型具有一定的临床获益。结论  中性粒细胞计数、HDL-C、LDL-C和基底节受累与MLI-VP密切相关，是独立危险因素。基于血液学指标和影像学特征的预测模型在本研究患者中表现出较好的判别能力，可用于早期高风险患者，为实施个体化精准干预提供依据。

关键词: 多发性腔隙性脑梗死, 血管性帕金森综合征, 预测模型, 危险因素, 血液学指标, 神经影像学特征

Abstract: Objective  To explore the related risk factors of vascular Parkinsonism (VP) in patients with multiple lacunar infarction (MLI), and to evaluate the application value of hematological indicators and neuroimaging features in predicting the risk of VP. Methods  A total of 169 patients with MLI admitted to Dunhua Hospital, Jilin Province from May 2021 to December 2024 were included. They were divided into the MLI group and the vascular Parkinsonism after multiple lacunar infarction (MLI-VP) group based on whether they developed VP. By using a random allocation method, the patients were divided into a model construction set (n=84) and an internal evaluation set (n=85). The clinical data and imaging features of the two groups were compared, and the risk factors for the occurrence of VP in MLI patients were screened. Then, a risk prediction model was constructed, and its performance and clinical application value were evaluated. Results  The results showed that neutrophil count, high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), and basal ganglia involvement were significantly associated with the occurrence of MLI-VP (P<0.05). Neutrophil count (OR=1.730, 95%CI:1.046-3.105, P=0.047), decreased HDL-C (OR=0.166, 95%CI:0.034-0.581, P=0.011), elevated LDL-C (OR=5.884, 95%CI:1.520-31.075, P=0.019), and basal ganglia involvement (OR=3.941, 95%CI:1.019-15.732, P=0.046) were all independent risk factors for MLI-VP. Based on these variables, a nomogram was constructed, and the area under the ROC curve was 0.892 (95%CI：0.806-0.892). The calibration curve showed good fit (Hosmer-Lemeshow P>0.05), and the decision curve analysis confirmed the model's clinical utility. Conclusion  Neutrophil count, HDL-C, LDL-C, and basal ganglia involvement are closely related to MLI-VP and are independent risk factors. Based on hematological indicators and imaging features, the prediction model shows good discriminative ability in this cohort, and it can be applied to early high-risk patients, providing a basis for implementing individualized precise intervention.

Key words: multiple lacunar cerebral infarction, vascular Parkinsonism, prediction model, risk factors, hematological indicators, neuroimaging features

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