切换至 "中华医学电子期刊资源库"

中华介入放射学电子杂志 ›› 2026, Vol. 14 ›› Issue (03) : 292 -298. doi: 10.3877/cma.j.issn.2095-5782.2026.03.010

论著

急性前循环大血管闭塞卒中取栓术后预后不良影响因素及预测模型构建
张菁菁1, 朱发勇2, 刘亚1,(), 潘晓虎1, 施学松1, 杨明刚1, 谢春明3, 赵国锋4   
  1. 1 211700 江苏盱眙,南京医科大学康达学院附属盱眙医院(江苏省盱眙县人民医院)神经内科
    2 223834 江苏宿迁,江苏省人民医院宿迁医院神经内科
    3 210029 江苏南京,东南大学附属中大医院神经内科
    4 210029 江苏南京,东南大学附属中大医院介入放射科
  • 收稿日期:2025-11-26 出版日期:2026-08-25
  • 通信作者: 刘亚
  • 基金资助:
    南京医科大学康达学院科研发展基金(KD2023KYJJ183); 国家卫生健康委能力建设和继续教育中心课题(GWJJMB202510021122)

Factors Influencing Poor Prognosis After Endovascular Thrombectomy for Acute Ischemic Stroke of Anterior Circulation Large Vessel Occlusion and Construction of a Risk Prediction Model

Jingjing Zhang1, Fayong Zhu2, Ya Liu1,(), Xiaohu Pan1, Xuesong Shi1, Minggang Yang1, Chunming Xie3, Guofeng Zhao4   

  1. 1 Department of neurology, XuYi Hospital Affiliated to Kangda College of Nanjing Medical University, Jiangsu Xuyi 211700
    2 Department of neurology, Suqian Hospital of Jiangsu Provincial People's Hospital, Jiangsu Suqian 223834, China
    3 Department of neurology, ZhongDa Hospital Affiliated with Southeast University, Jiangsu Nanjing 210029, China
    4 Department of Interventional Radiology, ZhongDa Hospital Affiliated with Southeast University, Jiangsu Nanjing 210029, China
  • Received:2025-11-26 Published:2026-08-25
  • Corresponding author: Ya Liu
引用本文:

张菁菁, 朱发勇, 刘亚, 潘晓虎, 施学松, 杨明刚, 谢春明, 赵国锋. 急性前循环大血管闭塞卒中取栓术后预后不良影响因素及预测模型构建[J/OL]. 中华介入放射学电子杂志, 2026, 14(03): 292-298.

Jingjing Zhang, Fayong Zhu, Ya Liu, Xiaohu Pan, Xuesong Shi, Minggang Yang, Chunming Xie, Guofeng Zhao. Factors Influencing Poor Prognosis After Endovascular Thrombectomy for Acute Ischemic Stroke of Anterior Circulation Large Vessel Occlusion and Construction of a Risk Prediction Model[J/OL]. Chinese Journal of Interventional Radiology(Electronic Edition), 2026, 14(03): 292-298.

目的

探讨前循环卒中机械取栓术(mechanical thrombectomy, MT)预后不良的影响因素,并构建预测模型。

方法

回顾性分析2020年1月至2024年12月江苏省人民医院宿迁医院、东南大学附属中大医院、南京医科大学康达学院附属盱眙人民医院行MT治疗的前循环急性大血管闭塞性卒中(acute large vessel occlusive stroke, ALVOS)患者的临床资料,根据术后90 d的预后情况分为预后良好组和不良预后组。分析基线资料、手术情况及围手术期并发症等,构建预测模型并验证其效能。

结果

共入组528例患者,再通成功496例。Logistic回归分析显示:年龄、就诊时间、美国国立卫生研究院卒中量表评分、取栓次数、再通成功、症状性颅内出血、术后24小时D-二聚体水平是预后不良的独立影响因素(P<0.05)。根据以上因素构建预测模型,校准曲线、决策曲线显示模型具有较好的准确度和临床效能。

结论

前循环ALVOS,根据年龄、就诊时间、D-二聚体、基线NIHSS评分、取栓次数、再通成功、症状性颅内出血构建的模型对不良预后的预测效能较好。

Objective

To explore the influencing factors of poor prognosis after mechanical thrombectomy for anterior circulation stroke and to construct prediction model.

Methods

Patients with anterior circulation ALVOS who underwent mechanical thrombectomy from January 2020 to December 2024 were divided into good prognosis group and poor prognosis group based on the 90-day postoperative prognosis. The baseline data, surgical conditions, and perioperative complications were analyzed. A clinical prediction model was constructed and its accuracy and efficacy were verified.

Results

A total of 528 patients were enrolled. Logistic regression analysis indicated that age, time of consultation, D-dimer, NIHSS score, number of thrombectomy, symptomatic intracranial hemorrhage, and successful reperfusion were independent influencing factors for poor prognosis (P< 0.05). Based on the above factors, a prediction model was constructed, and the calibration curve and decision curve showed that the model had good efficacy.

Conclusion

The model constructed for anterior circulation ALVOS based on age, time of consultation, D-dimer, baseline NIHSS score, number of thrombectomy, successful reperfusion, and symptomatic intracranial hemorrhage has good predictive efficacy for poor prognosis.

表1 预后良好组与不良预后组基线资料比较
表2 预后良好组与不良预后组手术参数比较
表3 预后良好组与不良预后组患者围手术期并发症的比较
表4 前循环ALVOS患者MT预后不良的二元Logistic回归分析
图1 ROC曲线评价模型预测MT预后不良的准确性(曲线下面积=0.864) ROC曲线:受试者工作特征曲线;MT:机械取栓术;AUC:曲线下面积。
图2 前循环ALVOS患者MT预后不良风险预测模型校准曲线 ALVOS:急性大血管闭塞性卒中;MT:机械取栓术。
图3 前循环ALVOS患者MT治疗预后不良风险预测模型决策曲线 ALVOS:急性大血管闭塞性卒中;MT:机械取栓术。
[1]
Hilkens NA, Casolla B, Leung TW, De Leeuw FE. Stroke[J]. Lancet, 2024, 403(10446): 2820-2836.
[2]
Cheng F, Yan B, Liao P, et al. Ischemic stroke and the biological hallmarks of aging[J]. Aging Dis, 2024, 16(5): 2908-2936.
[3]
Mendelson SJ, Prabhakaran S. Diagnosis and management of transient ischemic attack and acute ischemic stroke: a review[J]. Jama, 2021, 325(11): 1088-1098.
[4]
Nguyen TN, Abdalkader M, Fishcher U, et al. Endovascular management of acute stroke[J]. Lancet, 2024, 404(10459): 1265-1278.
[5]
Chen H, Lee JS, Michel P, et al. Endovascular stroke thrombectomy for patients with large ischemic core: a review[J]. JAMA Neurol, 2024, 81(10): 1085-1093.
[6]
Li Q, Abdalkader M, Siegler JE, et al. Mechanical thrombectomy for large ischemic stroke: a systematic review and meta-analysis[J]. Neurology, 2023, 101(9): e922-e932.
[7]
Li R, Tao C, Sun J, et al. Endovascular vs Medical management of acute basilar artery occlusion: a secondary analysis of a randomized clinical trial[J]. JAMA Neurol, 2024, 81(10):1043-1050.
[8]
Tao C, Nogueira RG, Zhu Y, et al. Trial of endovascular treatment of acute basilar-artery occlusion[J]. N Engl J Med, 2022, 387(15): 1361-1372.
[9]
Goyal M, Ospel JM, Ganesh A, et al. Endovascular treatment of stroke due to medium-vessel occlusion[J]. N Engl J Med, 2025, 392(14): 1385-1395.
[10]
Jovin TG, Li C, Wu L, et al. Trial of thrombectomy 6 to 24 hours after stroke due to basilar-artery occlusion[J]. N Engl J Med, 2022, 387(15): 1373-1384.
[11]
Sarraj A, Kleinig TJ, Hassan AE, et al. Association of endovascular thrombectomy vs medical management with functional and safety outcomes in patients treated beyond 24 hours of last known well: the SELECT late study[J]. JAMA Neurol, 2023, 80(2): 172-182.
[12]
Mitchell PJ, Yan B, Churilov L, et al. Endovascular thrombectomy versus standard bridging thrombolytic with endovascular thrombectomy within 4·5 h of stroke onset: an open-label, blinded-endpoint, randomised non-inferiority trial[J]. Lancet, 2022, 400(10346): 116-125.
[13]
Winkelmeier L, Kniep H, Faizy T, et al. Age and functional outcomes in patients with large ischemic stroke receiving endovascular thrombectomy[J]. JAMA Netw Open, 2024, 7(8): e2426007.
[14]
霍晓川, 高峰. 急性缺血性卒中血管内治疗中国指南2023[J]. 中国卒中杂志, 2023, 18(6): 684-711.
[15]
Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment[J]. Stroke, 1993, 24(1): 35-41.
[16]
Mbarek L, Jin A, Pan Y, et al. Stroke Prognosis: The impact of combined thrombotic, lipid, and inflammatory markers[J]. J Atheroscler Thromb, 2025, 32(4): 458-473.
[17]
Shen H, Killingsworth MC, Bhaskar SMM. Comprehensive meta-analysis of futile recanalization in acute ischemic stroke patients undergoing endovascular thrombectomy: prevalence, factors, and clinical outcomes[J]. Life, 2023, 13(10): 1965.
[18]
Wang LR, Li BH, Zhang Q, et al. Predictors of futile recanalization after endovascular treatment of acute ischemic stroke[J]. BMC Neurology, 2024, 24(1): 207.
[19]
Aldriweesh MA, Aldbas AA, Khojah O, et al. Clinical characteristics, risk factors, and outcomes of posterior circulation stroke: a retrospective study between younger and older adults in Saudi Arabia[J]. J Stroke Cerebrovasc Dis, 2024, 33(6): 107676.
[20]
Nagaraja N, Patel UK, Chaturvedi S. Age differences in utilization and outcomes of tissue-plasminogen activator and mechanical thrombectomy in acute ischemic stroke[J]. J Neurol Sci, 2021, 420: 117262.
[21]
Putaala J. Ischemic stroke in young adults[J]. Continuum (Minneap Minn), 2020, 26(2): 386-414.
[22]
Kaesmacher J, Cavalcante F, Kappelhof M, et al. Time to treatment with intravenous thrombolysis before thrombectomy and functional outcomes in acute ischemic stroke: a meta-analysis[J]. JAMS, 2024, 331(9): 764-777.
[23]
Man S, Solomon N, Macgrory B, et al. Shorter door-to-needle times are associated with better outcomes after intravenous thrombolytic therapy and endovascular thrombectomy for acute ischemic stroke[J]. Circulation, 2023, 148(1): 20-34.
[24]
Gao J, Wen C, Sun J, et al. Prognostic factors for acute posterior circulation cerebral infarction patients after endovascular mechanical thrombectomy: a retrospective study[J]. Medicine (Baltimore), 2022, 101(17): e29167.
[25]
何中海, 贾振宇, 刘圣. 血栓负荷评分对支架取栓治疗急性前循环大血管闭塞首过效应的预测价值[J/OL]. 中华介入放射学电子杂志, 2024, 12(4): 338-343.
[26]
Bao Q, Zhang J, Wu X, et al. Clinical significance of plasma D-dimer and fibrinogen in outcomes after stroke: a systematic review and meta-analysis[J]. Cerebrovasc Dis, 2023, 52(3): 318-343.
[27]
Choi K H, Kim J H, Kim J M, et al. D-dimer level as a predictor of recurrent stroke in patients with embolic stroke of undetermined source[J]. Stroke, 2021, 52(7): 2292-2301.
[28]
Hisamitsu Y, Kubo T, Fudaba H, et al. High D-dimer concentration is a significant independent prognostic factor in patients with acute large vessel occlusion undergoing endovascular thrombectomy[J]. World Neurosurg, 2022, 160: e487-e493.
[1] 苏金铭, 田萍, 李青林, 马静, 宋伟伟, 刘博文, 曹晓莹, 薛玲, 王文军. HER-2阳性乳腺癌患者复发转移的影响因素及风险预测模型的构建[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(04): 228-235.
[2] 丁彩霞, 曲景辉, 裴英宏, 李静娜, 郑晓瑜, 徐岭植, 李思思. 基于RNA结合蛋白基因表达特征的乳腺癌预后预测模型构建及评价分析[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(03): 138-147.
[3] 周文考, 袁丽, 任晓媛, 谢强, 苏力德, 闫敏, 陈智浩, 黄灵炎. 基于随机森林与LASSO回归的急性心肌梗死PCI术后3年不良事件预测模型构建与验证[J/OL]. 中华危重症医学杂志(电子版), 2026, 19(02): 122-130.
[4] 卢帅, 陈建明, 李敏娟, 单祎宁, 曹仁巍, 查晔军, 蒋协远. 基于NHANES数据库分析抑郁与骨质疏松性骨折愈合不良的关联及预测模型构建与验证[J/OL]. 中华损伤与修复杂志(电子版), 2026, 21(03): 167-176.
[5] 鲁峰刚, 刘宇, 张毅, 王秦玉. 新辅助放化疗直肠癌根治术患者术后吻合口漏影响因素分析及风险预测模型构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 347-351.
[6] 袁强, 范慈勃, 韩丽丽, 陈光, 陈纲, 赵锁. 融合病理图像与报告的多模态模型用于乳腺癌预后预测[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 374-378.
[7] 王珩, 马金曼, 王弼偲. 腹主动脉瘤腔内修复术后并发症的风险因素分析及预测模型构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(03): 292-295.
[8] 贺智恒, 姚德炯, 孙东方. 腹腔镜下胆囊切除术后胆瘘影响因素分析及风险预测模型的构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(02): 175-178.
[9] 邓瑞锋, 程璐, 刘远灵, 郑秋平, 刘溪, 江文聪, 江敏耀, 习明. 基于Logistic回归构建一期输尿管通路鞘置入失败的预测模型[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(02): 171-178.
[10] 柯雨仙, 刘罡, 郭莹莹, 何李谦, 姜轶. 慢性阻塞性肺疾病急性加重期并发呼吸衰竭风险预测模型研究进展[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(03): 500-503.
[11] 王, 冯恺源, 杨焮宇, 李鑫, 王芳, 胡褒曼, 曹国强, 李力. 基于CT量化评估胸部主要解剖单元病征的慢性阻塞性肺疾病患者5年全因死亡风险预测模型研究[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(02): 240-246.
[12] 王小振, 陈灿辉, 唐善华, 代浩嘉, 丰扬舸, 王恺, 李清平, 李川江. 基于不同机器学习技术构建肝移植术后早期严重并发症预测模型和效能比较[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(02): 197-204.
[13] 马凯蒂, 哈团结. 基于机器学习的糖尿病肾脏病患者疲乏状态诊断预测模型构建[J/OL]. 中华肾病研究电子杂志, 2026, 15(03): 144-150.
[14] 何慧, 邵昊, 曲利军. 成年人屈光不正合并干眼风险预测模型构建与验证的临床研究[J/OL]. 中华眼科医学杂志(电子版), 2026, 16(02): 91-96.
[15] 贾磊, 蔡莹, 陆锦琪, 时粉娟, 冯勤丽, 张在宏. 老年结直肠癌患者术后腹腔感染危险因素分析及预测模型构建[J/OL]. 中华老年病研究电子杂志, 2026, 13(02): 27-32.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?