[1]李其玲,刘敏,李军,等.急性脑梗死溶栓治疗发生恶性脑水肿列线图预测模型的构建及验证[J].中国临床神经外科杂志,2024,29(11):670-674.[doi:10.13798/j.issn.1009-153X.2024.11.007]
　LI Qi-ling,LIU Min,LI Jun,et al.Construction and verification of a nomogram prediction model for the occurrence of malignant cerebral edema following thrombolytic treatment of acute cerebral infarction[J].,2024,29(11):670-674.[doi:10.13798/j.issn.1009-153X.2024.11.007]
点击复制

急性脑梗死溶栓治疗发生恶性脑水肿列线图预测模型的构建及验证()
分享到：

《中国临床神经外科杂志》[ISSN:1009-153X/CN:42-1603/TN]

卷:
29
期数:
2024年11期
页码:
670-674
栏目:
论著
出版日期:
2024-11-30

文章信息/Info

Title:
Construction and verification of a nomogram prediction model for the occurrence of malignant cerebral edema following thrombolytic treatment of acute cerebral infarction
文章编号:
1009-153X（2024）11-0670-05
作者:
李其玲; 刘敏; 李军; 陈洪涛; 郭怀杰; 陈建霞
646100四川泸州，泸县人民医院神经内科（李其玲、刘敏、李军、陈洪涛、郭怀杰、陈建霞）
Author(s):
LI Qi-ling;  LIU Min;  LI Jun;  CHEN Hong-tao;  GUO Hua-ijie;  CHEN Jian-xia
Department of Neurology, Luxian County People's Hospital, Luzhou 646100, China
关键词:
急性脑梗死; 溶栓治疗; 恶性脑水肿; 危险因素; 列线图模型
Keywords:
Acute cerebral infarction;  Thrombolytic treatment;  Malignant cerebral edema;  Risk factors;  Nomogram model
分类号:
R 743
DOI:
10.13798/j.issn.1009-153X.2024.11.007
文献标志码:
A
摘要:
目的 探讨急性脑梗死（ACI）溶栓治疗发生恶性脑水肿的危险因素，并构建列线图预测模型。方法 回顾性分析2019年10月至2022年9月采用溶栓治疗的233例ACI的临床资料。溶栓治疗后72 h内意识水平下降或神经功能恶化，头颅MRI或CT显示脑梗死范围超过1/2大脑中动脉供血区伴中线移位>5 mm诊断为恶性脑水肿。采用多因素logistic回归模型分析恶性脑水肿的危险因素，采用R软件包构建列线图模型，采用Bootstrap法验证；绘制受试者工作特征（ROC）曲线评估列线图模型的预测效能。结果 233例中，50例发生恶性脑水肿，发生率为21.5%。多因素logistic回归分析显示，年龄（OR=1.238；95% CI 1.081~1.419；P=0.002）、脑梗死面积（OR=1.912；95% CI 1.115~3.280；P=0.019）、发病至溶栓时间（OR=9.828；95% CI 1.829~52.815；P=0.008）、白细胞计数（OR=2.289；95% CI 1.376~3.809；P=0.001）、溶栓前NIHSS评分（OR=6.503；95% CI 2.318~18.245；P<0.001）是ACI溶栓治疗发生恶性脑水肿的独立危险因素。基于多因素logistic回归分析结果，R软件成功构建列线图预测模型，Bootstrap法验证显示列线图模型校正曲线趋近于理想曲线（一致性指数为0.746；P=0.459）；ROC曲线分析显示列线图模型预测ACI溶栓治疗发生恶性脑水肿风险曲线下面积为0.977（95% CI 0.951~0.999），预测效能高。结论 年龄、发病至溶栓时间、脑梗死面积、白细胞计数、溶栓前NIHSS评分是影响ACI溶栓治疗并发恶性脑水肿的独立危险因素。基于这些危险因素构建的列线图风险预测模型对ACI溶栓治疗发生恶性脑水肿有较高的预测效能。
Abstract:
Objective To explore the risk factors of malignant cerebral edema (MCE) following thrombolytic treatment for acute cerebral infarction (ACI) and to construct a nomogram prediction model. Methods The clinical data of 233 patients with ACI who underwent thrombolytic treatment from October 2019 to September 2022 were retrospectively analyzed. MCE was defined as a decline in the level of consciousness or deterioration of neurological function within 72 hours after thrombolytic treatment, and the area of cerebral infarction exceeding half of the territory supplied by the middle cerebral artery with a midline shift >5 mm as shown by head MRI or CT. A multivariate logistic regression model was employed to analyze the independent risk factors for MCE. A nomogram prediction model was constructed using the R software package and internally validated by the Bootstrap method. The receiver operating characteristic (ROC) curve was plotted to assess the predictive efficacy of the model. Results Of the 233 patients, 50 patients (21.5%) developed MCE. Multivariate logistic regression analysis revealed that age (OR=1.238; 95% CI 1.081~1.419; P=0.002), the area of cerebral infarction (OR=1.912; 95% CI 1.115~3.280; P=0.019), the time from onset to thrombolysis (OR=9.828; 95% CI 1.829~52.815; P=0.008), white blood cell (OR=2.289; 95% CI 1.376~3.809; P=0.001), and the NIHSS score before thrombolysis (OR=6.503; 95% CI 2.318~18.245; P<0.001) were independent risk factors for MCE after thrombolytic treatment for ACI. Based on the above findings, a nomogram prediction model was successfully constructed. The Bootstrap method validation indicated that the calibration curve of the model approached the ideal curve (the concordance index was 0.746; P=0.459). The ROC curve analysis demonstrated that the area under the curve for predicting the risk of MCE after thrombolytic treatment for ACI was 0.977 (95% CI 0.951~0.999), indicating high predictive efficacy. Conclusions Age, the time from onset to thrombolysis, the area of cerebral infarction, white blood cell, and the NIHSS score before thrombolysis are independent risk factors for MCE after thrombolytic treatment for ACI. The nomogram risk prediction model constructed based on these factors has a high predictive efficacy for MCE after thrombolytic treatment for ACI.

参考文献/References:

[1] ZHANG S, GAO L, WO X, et al. Clinical observation of mild hypothermia combined with intravenous thrombolysis in treating patients with acute cerebral infarction [J]. Pak J Med Sci, 2021, 37(7): 1813-1818.
[2] YUAN Q, YU L, WANG F. Efficacy of using thromboelastography to detect coagulation function and platelet function in patients with acute cerebral infarction [J]. Acta Neurol Belg, 2021, 121(6): 1661-1667.
[3] JIANG QM, YU S, DONG XF, et al. Predictors and dynamic nomogram to determine the individual risk of malignant brain edema after endovascular thrombectomy in acute ischemic stroke [J]. J Clin Neurol, 2022, 18(3): 298-307.
[4] WANG C, ZHU Q, CUI T, et al. Early prediction of malignant edema after successful recanalization in patients with acute ischemic stroke [J]. Neurocrit Care, 2022, 36(3): 822-830.
[5] DHAR R, HAMZEHLOO A, KUMAR A, et al. Hemispheric CSF volume ratio quantifies progression and severity of cerebral edema after acute hemispheric stroke [J]. J Cereb Blood Flow Metab, 2021, 41(11): 2907-2915.
[6] YI X, ZHOU Q, QING T, et al. 20-hydroxyeiscosatetraenoic acid may be as a predictor of malignant middle cerebral artery infarction in patients with massive middle cerebral artery infarction [J]. BMC Neurol, 2021, 21(1): 437-461.
[7] YAN L, LI JY, CHEN YF, et al. Influencing factors of establishment of collateral circulation in patients with acute ischemic stroke and its construction of nomogram model [J]. Stroke Nerv Dis, 2022, 29 (2): 123-127. 晏立娜，李佳艳，陈奕菲，等. 急性脑梗死患者侧支循环建立的影响因素及其列线图模型构建[J]. 卒中与神经疾病，2022，29（2）：123-127.
[8] HUANG ZX, LI YK, LI SZ, et al. A dynamic nomogram for 3-month prognosis for acute ischemic stroke patients after endovascular therapy: a pooled analysis in southern China [J]. Front Aging Neurosci, 2021, 13(1): 1-23.
[9] Chinese Society of Neurology, Chinese Stroke Society. Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018 [J]. Chin J Neurol, 2018, 51(9): 666-682. 中华医学会神经病学分会，中华医学会神经病学分会脑血管病学组. 中国急性缺血性脑卒中诊治指南2018[J]. 中华神经科杂志，2018，51（9）：666-682.
[10] BRINJIKJI W, MADALINA-MEREUTA O, DAI D, et al. Mechanisms of fibrinolysis resistance and potential targets for thrombolysis in acute ischaemic stroke: lessons from retrieved stroke emboli [J]. Stroke Vasc Neurol, 2021, 6(4): 658-667.
[11] GU Y, ZHOU C, PIAO Z, et al. Cerebral edema after ischemic stroke: pathophysiology and underlying mechanisms [J]. Front Neurosci, 2022, 16(1): 1-49.
[12] ZHANG X, HUANG P, ZHANG R. Evaluation and prediction of post-stroke cerebral edema based on neuroimaging [J]. Front Neurol, 2022, 12(1): 1-38.
[13] LIU XZ. Influencing factors of cerebral edema after thrombolytic therapy in patients with acute cerebral infarction [J]. Henan Med Res, 2021, 30(21): 3947-3950. 刘雪征. 急性脑梗死患者溶栓治疗后发生脑水肿的影响因素[J]. 河南医学研究，2021，30（21）：3947-3950.
[14] WANG YY. Logistic regression analysis of related factors for cerebral edema in patients with acute cerebral infarction after thrombolytic therapy [J]. J Aerospace Med, 2021, 32(11): 1324-1326. 王月云. 急性脑梗死患者溶栓治疗后发生脑水肿的相关因素Logistic回归分析[J]. 航空航天医学杂志，2021，32（11）：1324-1326.
[15] JIANG L, ZHANG C, WANG S, et al. MRI radiomics features from infarction and cerebrospinal fluid for prediction of cerebral edema after acute ischemic stroke [J]. Front Aging Neurosci, 2022, 14(1): 1-35.
[16] IBRAHIM F, MENEZES S, BUHNERKEMPE M, et al. Systemic white blood cell count as a biomarker for malignant cerebral edema in large vessel ischemic MCA stroke [J]. J Stroke Cerebrovasc Dis, 2022, 31(8): 1-13.
[17] GARAVELLI F, GHELFI AM, KILSTEIN JG. Usefulness of NIHSS score as a predictor of non-neurological in-hospital complications in stroke [J]. Med Clin (Barc), 2021, 157(9): 434-437.
[18] TONG J, PENG MY, CHEN GZ, et al. Nomogram prediction model construction of malignant brain edema after endovascular treatment in acute stroke [J]. Chin J CT MRI, 2023, 21(1): 32-35. 童　俊，彭明洋，陈国中，等. 急性脑卒中血管内治疗后恶性脑水肿风险的列线图预测模型构建[J]. 中国CT和MRI杂志，2023，21（1）：32-35.

相似文献/References:

[1]关玉华　艾克拜尔·加马力　曹莹莹.超选择性动脉溶栓治疗急性脑梗死的临床研究[J].中国临床神经外科杂志,2015,(04):222.[doi:10.13798/j.issn.1009-153X.2015.04.010]
　GUAN Yu-hua,JIAMALI AiKkebaier,CHEN Shi-bao,et al.Superselective intra-arterial thrombolytic therapy early after infraction in 217 patients with actue cerebral infraction[J].,2015,(11):222.[doi:10.13798/j.issn.1009-153X.2015.04.010]
[2]闫忠军　徐东为　胡　焱　张玉凯　杨建龙.急性缺血性脑卒中的治疗体会[J].中国临床神经外科杂志,2017,(08):591.[doi:10.13798/j.issn.1009-153X.2017.08.025]
[3]徐东为　赵振伟　李　江　闫忠军　屈　延　高国栋.急性前循环脑动脉闭塞支架成形术的疗效[J].中国临床神经外科杂志,2018,(05):328.[doi:10.13798/j.issn.1009-153X.2018.05.008]
　XU Dong-wei,ZHAO Zhen-wei,LI Jiang,et al.Effect of stenting on acute cerebral artery occlusion in anterior circulation[J].,2018,(11):328.[doi:10.13798/j.issn.1009-153X.2018.05.008]
[4]水庆学　孙洪娣　田落意.重型颅脑损伤开颅术后继发急性脑梗死的危险因素[J].中国临床神经外科杂志,2019,(10):624.[doi:10.13798/j.issn.1009-153X.2019.10.015]
[5]吴海强　李钟安　赖　勇等.脊髓前动脉代偿良好的急性基底动脉闭塞介入治疗1例[J].中国临床神经外科杂志,2021,26(12):975.[doi:10.13798/j.issn.1009-153X.2021.12.029]

备注/Memo

备注/Memo:
（2023-08-03收稿，2024-04-10修回）
基金项目：2021年泸县经济信息科学技术项目（LXYJKF-2021-12）
通信作者：陈建霞，Email：25155312@qq.com
更新日期/Last Update: 2024-11-30