Article(id=1322630816358842824, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.18.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1767715200000, receivedDateStr=2026-01-07, revisedDate=1786291200000, revisedDateStr=2026-08-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1793269359912, onlineDateStr=2026-10-29, pubDate=1790265600000, pubDateStr=2026-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793269359912, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793269359912, creator=13701087609, updateTime=1793269359912, updator=13701087609, issue=Issue{id=1322630794611380728, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='18', pageStart='3153', pageEnd='3344', issueExtLink='null', onlineDate='null', pubDate='1790265600000', pubDateStr='2026-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793269354727, creator='13701087609', updateTime=1793343080645, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940023645011984, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940023645011985, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3271, endPage=3276, ext={EN=ArticleExt(id=1322630816593723849, articleId=1322630816358842824, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of a risk prediction model for lower extremity deep vein thrombosis after mechanical thrombectomy in patients with acute ischemic stroke, columnId=1220689488284668633, journalTitle=Chinese Nursing Research, columnName=Comprehensive Study, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the incidence and influencing factors of lower extremity deep vein thrombosis(DVT) after intracranial mechanical thrombectomy in patients with acute ischemic stroke(AIS),and to construct a risk prediction model.

Methods

A total of 315 patients admitted to the neurointensive care unit of Nanfang Hospital Southern Medical University were enrolled as study subjects from January 2023 to September 2024. Univariate and multivariate Logistic regression analyses were performed to explore the influencing factors for DVT.A Nomogram prediction model was constructed.The prediction model was evaluated using the receiver operating characteristic(ROC) curve,Hosmer-Lemeshow test,calibration curve,and clinical decision curve analysis(DCA).

Results

The incidence of lower extremity DVT after mechanical thrombectomy in the 315 AIS patients was 28.89%(91/315).Logistic regression analysis showed that age,National Institute of Health Stroke Scale(NIHSS) score,D-dimer level,puncture time,and operation time were influencing factors for lower extremity DVT after mechanical thrombectomy in AIS patients(all P<0.05).A Nomogram model was constructed based on these influencing factors.The area under the ROC curve was 0.916(95% CI 0.883-0.949).The Youden index was 0.675.The sensitivity was 0.912.The specificity was 0.763.The Bootstrap test indicated good model calibration.Clinical DCA demonstrated good clinical applicability within the threshold probability range of 0.01-0.90.

Conclusions

Age,NIHSS score,D-dimer level,puncture time,and operation time were identified as factors affecting the occurrence of lower extremity DVT after mechanical thrombectomy in AIS patients.The Nomogram model constructed based on influencing factors demonstrated good predictive performance and may serve as a quantitative tool for nurses to identify high-risk patients at an early stage.

, authors=Ya'nan WU, Chaoran XU, Meng CAO, Yansi LUO, Qindi ZHANG, Lu YANG, Daihong CHENG, Yixia ZHOU, Xiaomei ZHANG, authorsList=Ya'nan WU, Chaoran XU, Meng CAO, Yansi LUO, Qindi ZHANG, Lu YANG, Daihong CHENG, Yixia ZHOU, Xiaomei ZHANG, authorCompany=null, correspAuthors=Xiaomei ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322630819294855634, articleId=1322630816358842824, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=急性缺血性脑卒中病人机械取栓术后下肢深静脉血栓形成风险预测模型构建, columnId=1220689488938980091, journalTitle=护理研究, columnName=综合研究, runingTitle=null, highlight=null, articleAbstract=
目的

探讨急性缺血性脑卒中(AIS)病人行颅内机械取栓术后下肢深静脉血栓(DVT)发生情况及其影响因素,并构建风险预测模型。

方法

以2023年1月—2024年9月南方医科大学南方医院神经重症监护病房收治的315例病人为研究对象,采用单因素和多因素Logistic回归分析探讨DVT发生的影响因素,并构建列线图预测模型。通过受试者工作特征曲线(ROC)、Hosmer-Lemeshow检验、校准曲线、临床决策曲线分析对预测模型进行评估。

结果

315例AIS病人机械取栓术后下肢DVT发生率为28.89%(91/315)。Logistic回归分析结果显示,年龄、美国国立卫生研究院卒中量表(NIHSS)评分、D-二聚体水平、穿刺时间及手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素(均P<0.05)。基于影响因素构建列线图模型,其ROC曲线下面积为0.916[95%CI(0.883,0.949)],约登指数为0.675,灵敏度为0.912,特异度为0.763。Bootstrap检验显示模型校准良好。临床决策曲线分析表明在阈值概率0.01~0.90范围内模型有良好的临床适用性。

结论

年龄、NIHSS评分、D-二聚体水平、穿刺时间及手术时间可影响AIS病人机械取栓术后下肢DVT的发生情况,基于此构建的列线图模型预测效能良好,可为护理人员早期识别高危病人提供量化工具。

, authors=吴亚男, 徐超冉, 曹猛, 罗彦嗣, 张钦缔, 杨露, 程代红, 周弈遐, 张晓梅*, *, authorsList=吴亚男, 徐超冉, 曹猛, 罗彦嗣, 张钦缔, 杨露, 程代红, 周弈遐, 张晓梅, authorCompany=null, correspAuthors=张晓梅, authorNote=

吴亚男,主管护师,本科

, correspAuthorsNote=
张晓梅,E-mail:
ZHANG Xiaomei, E-mail:
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rfNumber=34, rfOrder=33, authorNames=Emil A C, Brown J C, Green J L, journalName=BMC Med Genomics, refType=null, unstructuredReference=Emil A C,Brown J C,Green J L,et al.A phenome-wide approach to identify causal risk factors for deep vein thrombosis[J].BMC Med Genomics,2023,16(1):284., articleTitle=A phenome-wide approach to identify causal risk factors for deep vein thrombosis, refAbstract=null), Reference(id=1322630829738672701, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, doi=null, pmid=null, pmcid=null, year=2022, volume=53, issue=7, pageStart=2562, pageEnd=2566, url=null, language=null, rfNumber=35, rfOrder=34, authorNames=Thomas D, Huyen T, Sanjeev C, journalName=Injury, refType=null, unstructuredReference=Thomas D,Huyen T,Sanjeev C,et al.Isolated distal DVT in trauma:a study of the management of isolated distal deep vein thrombosis acquired as an inpatient in trauma patients[J].Injury,2022,53(7):2562-2566., articleTitle=Isolated distal DVT in trauma:a study of the management of isolated distal deep vein thrombosis acquired as an inpatient in trauma patients, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322630819504570835, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, xref=1, ext=[AuthorCompanyExt(id=1322630819517153748, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, companyId=1322630819504570835, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Nanfang Hospital, Southern Medical University, Guangdong 510515 China), AuthorCompanyExt(id=1322630819525542357, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, companyId=1322630819504570835, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1南方医科大学南方医院,广东 510515)])], figs=[ArticleFig(id=1322630822688047629, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Figure 1, caption=Nomogram model, figureFileSmall=gYVdn3wKh7v1piAugHVm6A==, figureFileBig=6ID9CAtseMYQuVUcf/1f5A==, tableContent=null), ArticleFig(id=1322630824390935054, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=图1, caption=列线图模型, figureFileSmall=gYVdn3wKh7v1piAugHVm6A==, figureFileBig=6ID9CAtseMYQuVUcf/1f5A==, tableContent=null), ArticleFig(id=1322630824592261647, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Figure 2, caption=ROC curve of the model, figureFileSmall=8bbWI4r6ujl0tcScrPtwFQ==, figureFileBig=3oVKseVc9MYJzTRXVaHWQw==, tableContent=null), ArticleFig(id=1322630824697119248, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=图2, caption=模型的ROC曲线, figureFileSmall=8bbWI4r6ujl0tcScrPtwFQ==, figureFileBig=3oVKseVc9MYJzTRXVaHWQw==, tableContent=null), ArticleFig(id=1322630824760033809, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Figure 3, caption=Calibration curve of the model, figureFileSmall=/KP76E6rPx59zI9Ciku79w==, figureFileBig=ibFes31heM7gaptUsRrh0g==, tableContent=null), ArticleFig(id=1322630824827142674, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=图3, caption=模型的校准曲线, figureFileSmall=/KP76E6rPx59zI9Ciku79w==, figureFileBig=ibFes31heM7gaptUsRrh0g==, tableContent=null), ArticleFig(id=1322630824902640147, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Figure 4, caption=DCA curve of the model, figureFileSmall=03qIVw6bozqhd8Nj7T8Zyg==, figureFileBig=i0asyBLhtEdcFkEK9sjOyg==, tableContent=null), ArticleFig(id=1322630824978137620, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=图4, caption=模型的DCA曲线, figureFileSmall=03qIVw6bozqhd8Nj7T8Zyg==, figureFileBig=i0asyBLhtEdcFkEK9sjOyg==, tableContent=null), ArticleFig(id=1322630825045246485, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Table 1, caption=

Distribution of lower extremity DVT after mechanical thrombectomy in AIS patients

, figureFileSmall=null, figureFileBig=null, tableContent=
血栓分布总例数右下肢左下肢双下肢
合计91353125
肌间静脉血栓76282523
近端DVT(腘静脉、股静脉、髂静脉)2200
远端DVT(胫前静脉、胫后静脉、腓静脉)11452
混合型DVT(肌间静脉+远端静脉或近端静脉)2110
), ArticleFig(id=1322630825120743958, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=表1, caption=

AIS病人机械取栓术后下肢DVT分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
血栓分布总例数右下肢左下肢双下肢
合计91353125
肌间静脉血栓76282523
近端DVT(腘静脉、股静脉、髂静脉)2200
远端DVT(胫前静脉、胫后静脉、腓静脉)11452
混合型DVT(肌间静脉+远端静脉或近端静脉)2110
), ArticleFig(id=1322630825196241431, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Table 2, caption=

Univariate analysis of influencing factors for lower extremity DVT after mechanical thrombectomy in AIS patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类DVT组(n=91)非DVT组(n=224)统计值P
性别[例(%)]男67(73.63)173(77.23)χ2=0.4640.496
女24(26.37)51(22.77)
静脉溶栓治疗[例(%)]是21(23.08)25(11.16)χ2=7.3680.007
否70(76.92)199(88.84)
年龄(岁)68.00(61.00,74.00)60.00(54.00,70.00)Z=-4.873<0.001
BMI(kg/m2)23.80(21.75,26.00)23.90(21.98,26.05)Z=-0.4860.627
NIHSS评分(分)15.00(7.00,19.00)5.50(2.00,13.00)Z=-6.614<0.001
D-二聚体(mg/L)3.78(1.42,8.38)0.55(0.29,1.85)Z=-8.205<0.001
吸烟史[例(%)]有46(50.55)141(62.95)χ2=4.1230.042
无45(49.45)83(37.05)
饮酒史[例(%)]有33(36.26)87(38.84)χ2=0.1820.670
无58(63.74)137(61.16)
高血压病史[例(%)]有55(60.44)134(59.82)χ2=0.0100.919
无36(39.56)90(40.18)
糖尿病病史[例(%)]有27(29.67)61(27.23)χ2=0.1910.662
无64(70.33)163(72.77)
心脏病病史[例(%)]有23(25.27)38(16.96)χ2=2.8620.091
无68(74.73)186(83.04)
肿瘤病史[例(%)]有10(10.99)18(8.04)χ2=0.6970.404
无81(89.01)206(91.96)
穿刺时间(min)8.00(2.00,9.00)2.00(1.00,4.00)Z=-6.686<0.001
手术时间(min)110.00(84.50,142.00)76.00(53.75,101.00)Z=-6.993<0.001
), ArticleFig(id=1322630825284321816, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=表2, caption=

AIS病人机械取栓术后发生下肢DVT影响因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类DVT组(n=91)非DVT组(n=224)统计值P
性别[例(%)]男67(73.63)173(77.23)χ2=0.4640.496
女24(26.37)51(22.77)
静脉溶栓治疗[例(%)]是21(23.08)25(11.16)χ2=7.3680.007
否70(76.92)199(88.84)
年龄(岁)68.00(61.00,74.00)60.00(54.00,70.00)Z=-4.873<0.001
BMI(kg/m2)23.80(21.75,26.00)23.90(21.98,26.05)Z=-0.4860.627
NIHSS评分(分)15.00(7.00,19.00)5.50(2.00,13.00)Z=-6.614<0.001
D-二聚体(mg/L)3.78(1.42,8.38)0.55(0.29,1.85)Z=-8.205<0.001
吸烟史[例(%)]有46(50.55)141(62.95)χ2=4.1230.042
无45(49.45)83(37.05)
饮酒史[例(%)]有33(36.26)87(38.84)χ2=0.1820.670
无58(63.74)137(61.16)
高血压病史[例(%)]有55(60.44)134(59.82)χ2=0.0100.919
无36(39.56)90(40.18)
糖尿病病史[例(%)]有27(29.67)61(27.23)χ2=0.1910.662
无64(70.33)163(72.77)
心脏病病史[例(%)]有23(25.27)38(16.96)χ2=2.8620.091
无68(74.73)186(83.04)
肿瘤病史[例(%)]有10(10.99)18(8.04)χ2=0.6970.404
无81(89.01)206(91.96)
穿刺时间(min)8.00(2.00,9.00)2.00(1.00,4.00)Z=-6.686<0.001
手术时间(min)110.00(84.50,142.00)76.00(53.75,101.00)Z=-6.993<0.001
), ArticleFig(id=1322630825368207897, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=EN, label=Table 3, caption=

Multivariate analysis of influencing factors for lower extremity DVT after mechanical thrombectomy in AIS patients

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变量回归系数标准误Wald χ2值POR值95%CI
常量-8.7491.32343.765<0.001
年龄0.0440.0167.7930.0051.045[1.013,1.078]
NIHSS评分0.0770.02113.451<0.0011.080[1.036,1.125]
D-二聚体0.1880.04815.246<0.0011.206[1.098,1.326]
穿刺时间0.2230.04031.103<0.0011.250[1.156,1.352]
手术时间0.0260.00529.140<0.0011.026[1.017,1.036]
), ArticleFig(id=1322630825452093978, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630816358842824, language=CN, label=表3, caption=

AIS病人机械取栓术后发生下肢DVT影响因素的多因素分析

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变量回归系数标准误Wald χ2值POR值95%CI
常量-8.7491.32343.765<0.001
年龄0.0440.0167.7930.0051.045[1.013,1.078]
NIHSS评分0.0770.02113.451<0.0011.080[1.036,1.125]
D-二聚体0.1880.04815.246<0.0011.206[1.098,1.326]
穿刺时间0.2230.04031.103<0.0011.250[1.156,1.352]
手术时间0.0260.00529.140<0.0011.026[1.017,1.036]
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急性缺血性脑卒中病人机械取栓术后下肢深静脉血栓形成风险预测模型构建
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吴亚男 , 徐超冉 , 曹猛 , 罗彦嗣 , 张钦缔 , 杨露 , 程代红 , 周弈遐 , 张晓梅 *, *
护理研究 | 综合研究 2026,40(18): 3271-3276
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护理研究 |综合研究 2026 , 40 (18) : 3271 -3276
急性缺血性脑卒中病人机械取栓术后下肢深静脉血栓形成风险预测模型构建
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吴亚男, 徐超冉, 曹猛, 罗彦嗣, 张钦缔, 杨露, 程代红, 周弈遐, 张晓梅*, *
作者信息
  • 1南方医科大学南方医院,广东 510515
通讯作者:
张晓梅,E-mail:
ZHANG Xiaomei, E-mail:
作者简介:

吴亚男,主管护师,本科

Construction of a risk prediction model for lower extremity deep vein thrombosis after mechanical thrombectomy in patients with acute ischemic stroke
Ya'nan WU, Chaoran XU, Meng CAO, Yansi LUO, Qindi ZHANG, Lu YANG, Daihong CHENG, Yixia ZHOU, Xiaomei ZHANG
Affiliations
  • 1Nanfang Hospital, Southern Medical University, Guangdong 510515 China
出版时间: 2026-09-25 doi: 10.12102/j.issn.1009-6493.2026.18.018
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目的

探讨急性缺血性脑卒中(AIS)病人行颅内机械取栓术后下肢深静脉血栓(DVT)发生情况及其影响因素,并构建风险预测模型。

方法

以2023年1月—2024年9月南方医科大学南方医院神经重症监护病房收治的315例病人为研究对象,采用单因素和多因素Logistic回归分析探讨DVT发生的影响因素,并构建列线图预测模型。通过受试者工作特征曲线(ROC)、Hosmer-Lemeshow检验、校准曲线、临床决策曲线分析对预测模型进行评估。

结果

315例AIS病人机械取栓术后下肢DVT发生率为28.89%(91/315)。Logistic回归分析结果显示,年龄、美国国立卫生研究院卒中量表(NIHSS)评分、D-二聚体水平、穿刺时间及手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素(均P<0.05)。基于影响因素构建列线图模型,其ROC曲线下面积为0.916[95%CI(0.883,0.949)],约登指数为0.675,灵敏度为0.912,特异度为0.763。Bootstrap检验显示模型校准良好。临床决策曲线分析表明在阈值概率0.01~0.90范围内模型有良好的临床适用性。

结论

年龄、NIHSS评分、D-二聚体水平、穿刺时间及手术时间可影响AIS病人机械取栓术后下肢DVT的发生情况,基于此构建的列线图模型预测效能良好,可为护理人员早期识别高危病人提供量化工具。

急性缺血性脑卒中  /  机械取栓  /  下肢深静脉血栓  /  危险因素  /  列线图
Objective

To investigate the incidence and influencing factors of lower extremity deep vein thrombosis(DVT) after intracranial mechanical thrombectomy in patients with acute ischemic stroke(AIS),and to construct a risk prediction model.

Methods

A total of 315 patients admitted to the neurointensive care unit of Nanfang Hospital Southern Medical University were enrolled as study subjects from January 2023 to September 2024. Univariate and multivariate Logistic regression analyses were performed to explore the influencing factors for DVT.A Nomogram prediction model was constructed.The prediction model was evaluated using the receiver operating characteristic(ROC) curve,Hosmer-Lemeshow test,calibration curve,and clinical decision curve analysis(DCA).

Results

The incidence of lower extremity DVT after mechanical thrombectomy in the 315 AIS patients was 28.89%(91/315).Logistic regression analysis showed that age,National Institute of Health Stroke Scale(NIHSS) score,D-dimer level,puncture time,and operation time were influencing factors for lower extremity DVT after mechanical thrombectomy in AIS patients(all P<0.05).A Nomogram model was constructed based on these influencing factors.The area under the ROC curve was 0.916(95% CI 0.883-0.949).The Youden index was 0.675.The sensitivity was 0.912.The specificity was 0.763.The Bootstrap test indicated good model calibration.Clinical DCA demonstrated good clinical applicability within the threshold probability range of 0.01-0.90.

Conclusions

Age,NIHSS score,D-dimer level,puncture time,and operation time were identified as factors affecting the occurrence of lower extremity DVT after mechanical thrombectomy in AIS patients.The Nomogram model constructed based on influencing factors demonstrated good predictive performance and may serve as a quantitative tool for nurses to identify high-risk patients at an early stage.

acute ischemic stroke  /  mechanical thrombectomy  /  lower extremity deep vein thrombosis  /  risk factors  /  Nomogram
吴亚男, 徐超冉, 曹猛, 罗彦嗣, 张钦缔, 杨露, 程代红, 周弈遐, 张晓梅. 急性缺血性脑卒中病人机械取栓术后下肢深静脉血栓形成风险预测模型构建. 护理研究, 2026 , 40 (18) : 3271 -3276 . DOI: 10.12102/j.issn.1009-6493.2026.18.018
Ya'nan WU, Chaoran XU, Meng CAO, Yansi LUO, Qindi ZHANG, Lu YANG, Daihong CHENG, Yixia ZHOU, Xiaomei ZHANG. Construction of a risk prediction model for lower extremity deep vein thrombosis after mechanical thrombectomy in patients with acute ischemic stroke[J]. Chinese Nursing Research, 2026 , 40 (18) : 3271 -3276 . DOI: 10.12102/j.issn.1009-6493.2026.18.018
急性缺血性脑卒中(acute ischemic stroke,AIS)是指因各种原因引起脑部血液供应障碍导致局部脑组织缺血、缺氧性坏死并出现相应神经功能缺损的一类临床综合征[1-3]。大血管闭塞(large vessel occlusion,LVO)所致的AIS可通过机械取栓实现血管再通,这是目前应用较多的血管内介入治疗手段,已成为AIS病人的主要治疗选择之一[4-7]。下肢深静脉血栓(deep venous thrombosis,DVT)是由多种原因导致血液在下肢深静脉系统内凝结形成血栓的一类血管疾病,可阻碍病人康复,增加医疗负担[8-10],且DVT发生后的血栓脱落还可引起致死性或非致死性肺栓塞[11],严重威胁病人生命健康安全。已有研究结果显示,AIS病人DVT发生率较高(45%)[12]。但针对AIS病人机械取栓术后这一特定情境下的DVT发生风险评估工具不足,不利于临床医护人员及时采取措施对高危人群进行甄别与管理。故本研究以南方医科大学南方医院神经重症监护病房收治的病人为研究对象,分析AIS病人机械取栓术后下肢DVT发生现状及其影响因素,并基于影响因素构建预测模型,以期为临床提供依据。
以2023年1月—2024年9月南方医科大学南方医院神经重症监护病房收治的315例病人为研究对象。纳入标准:1)年龄≥18岁;2)经计算机断层扫描(CT)或磁共振成像(MRI)等影像学检查明确诊断为AIS;3)血管影像学检查证实存在颅内大血管闭塞,符合急诊机械取栓术;4)在症状出现后24 h内行急诊机械取栓术;5)病人或其授权家属对机械取栓术治疗知情同意;6)病历资料完整。排除标准:1)入院时影像学检查显示脑实质出血或蛛网膜下腔出血;2)住院期间死亡;3)有静脉血栓栓塞(VTE)病史;4)合并凝血功能障碍;5)临床资料不全;6)住院时间<3 d。本研究已通过南方医科大学南方医院伦理委员会审批(批号:NFEC-2025-533),因研究为回顾性设计且不对病人造成额外风险,伦理委员会批准豁免知情同意。
1)结局变量(因变量):以下肢DVT发生作为结局变量,于病人术后3 d进行双下肢深静脉彩色多普勒超声检查,诊断标准参照《深静脉血栓形成的诊断和治疗指南(第三版)》[13]。2)暴露变量(自变量):实验室指标(术后24 h内复查D-二聚体水平)、手术相关指标(如穿刺时间、手术时间)、人口学特征[如性别、年龄、体质指数(BMI)]、神经功能缺损程度[于病人入院时采用美国国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评估]、个人史与既往史(如吸烟史、饮酒史、高血压病史、糖尿病病史、心脏病病史、肿瘤病史)等。其中,穿刺时间指从病人手术体位准备完毕至成功置入股动脉血管鞘的时间;手术时间指从动脉穿刺成功至手术操作结束时间。
调取院内机械取栓AIS病人病历342份,剔除资料缺失、下肢静脉彩超检查不完善病例27份,最终纳入有效病例315份。由3名经过统一培训的研究者独立完成电子病历数据提取,收集病人一般人口学、基础病史、手术操作、术后检验及下肢静脉超声检查等结果并交叉核对,存在数据不一致处查阅原始病历修正,核对数据后建立数据库。
使用SPSS 26.0软件进行统计学分析。符合正态分布的定量资料数据用均数±标准差(x±s)表示,组间比较采用独立样本t检验;不符合正态分布的定量资料数据用中位数(四分位数)[M(P25,P75)]表示,组间比较采用Mann-Whitney U检验;定性资料用频数及百分比(%)表示,组间比较采用χ2检验;采用Logistic回归分析探讨AIS病人行机械取栓术后发生下肢DVT的影响因素。基于AIS病人机械取栓术后发生下肢DVT的影响因素构建列线图模型,采用受试者工作特征(ROC)曲线下面积(AUC)、Hosmer-Lemeshow检验、校准曲线、临床决策曲线分析(DCA)对模型进行评估。以P<0.05为差异有统计学意义。
315例AIS病人机械取栓术后下肢DVT发生率为28.89%(91/315)。在发生下肢DVT的病人中,肌间静脉血栓发生率较高,为83.52%(76/91),其中,右下肢发生肌间静脉血栓的病人为28例(占肌间静脉血栓的36.84%),左下肢发生肌间静脉血栓的病人为25例(占肌间静脉血栓的32.89%),双下肢发生肌间静脉血栓的病人为23例(占肌间静脉血栓的30.26%)。91例发生下肢DVT的病人DVT肢体分布情况显示,右下肢占38.46%(35/91),左下肢占34.07%(31/91),双下肢占27.47%(25/91),详见表1。
将单因素分析中有统计学意义(P<0.05)的变量作为自变量,将AIS病人机械取栓术后下肢DVT发生情况作为因变量(未发生=0,发生=1),进行Logistic回归分析,结果显示,年龄、NIHSS评分、D-二聚体水平、穿刺时间及手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素(均P<0.05)。见表3。
基于AIS病人机械取栓术后发生下肢DVT的影响因素构建列线图模型(见图1),该模型AUC为0.916[95%CI(0.883,0.949)],约登指数为0.675,灵敏度为0.912,特异度为0.763,见图2。
Bootstrap法内部验证结果显示,模型区分度良好,一致性指数(C-index)为0.902[95%CI(0.857,0.947)]。校准曲线分析显示,模型预测下肢DVT发生风险的平均绝对误差为0.038,Hosmer-Lemeshow拟合优度检验结果显示,χ2=5.721,P=0.677,表明模型预测概率与实际观测概率间具有良好的一致性。见图3。
DCA结果显示,在0.01~0.90的阈值概率范围内,应用模型进行决策带来的净获益高于对所有病人进行干预或不对任何病人进行干预。见图4。
大血管闭塞性AIS病人往往病情较重、预后较差,需早期行血管内治疗[7,14]。但既往研究主要聚焦于术后神经系统并发症[15-18],针对术后下肢DVT研究证据较少。本研究结果显示,AIS病人机械取栓术后下肢DVT发生率为28.89%(以肌间静脉血栓为主、右侧肢体高发),高于神经重症病人[19]及一般AIS病人[20-21],与出血性脑卒中病人[21]相近。提示了解AIS病人机械取栓术后下肢DVT发生的危险因素,并制定有效的风险评估工具具有重要意义。目前,临床通用的DVT发生风险评估工具对机械取栓术后AIS病人的针对性不足,本研究构建针对性的风险预测模型,有利于为精准防控AIS病人机械取栓术后下肢DVT发生提供依据。
本研究Logistic回归分析结果显示,年龄是AIS病人机械取栓术后发生下肢DVT的影响因素(P=0.005),病人年龄每增加1岁,机械取栓术后发生下肢DVT的风险约升高4.5%,与脑卒中相关研究结果相似[13,22]。随着年龄增长,病人腓肠肌等肌肉泵的“促回流”作用减弱,血液瘀滞程度加剧[23-24];且老年人血液中凝血因子水平与纤溶系统不平衡也易导致血栓形成[25]。提示临床需重视高龄AIS病人机械取栓术后的下肢DVT防控,早期开始进行康复运动是预防深静脉血栓的有效方法[26-27]。同时,对于高龄AIS病人除应术后早期启动预防性干预(如床上关节活动度练习、床上良肢位保持、使用间歇性气压治疗等),必要时还可结合病人出血风险评估结果,合理使用预防剂量的肝素等药物预防,同时加强床旁超声筛查频率,以实现血栓的早期识别与管理。
本研究Logistic回归分析结果显示,NIHSS评分是AIS病人机械取栓术后发生下肢DVT的影响因素(P<0.001)。已有研究结果显示,NIHSS评分越高下肢DVT发生风险越高[28]。原因可能为NIHSS评分较高意味着神经功能缺损较严重,此类病人可能伴随吞咽困难、意识障碍、肢体活动障碍等并发症,导致液体摄入不足,血液黏稠度升高,加之术后绝对卧床休息、右下肢制动,易使静脉血流速度减慢,进一步加剧血栓形成[25,29]。对此,护理人员应及早启动下肢DVT预防干预措施,如术后6 h内启动踝泵运动。
本研究Logistic回归分析结果显示,D-二聚体水平是AIS病人机械取栓术后发生下肢DVT的影响因素(P<0.001)。已有研究显示,D-二聚体水平诊断下肢DVT的灵敏度较高,准确率高达95%[22,30]。D-二聚体作为凝血与纤溶活化标志物,水平升高提示血栓前状态[31-32],机械取栓术为侵入性血管内操作,可能导致血管内膜破坏,激活局部凝血机制,引起血小板聚集、黏附形成血栓[33-34]。提示术后动态监测D-二聚体水平,有利于临床工作者,尤其是护理人员进行下肢DVT风险分层,为个体化预防策略的制定提供数据支持。
本研究Logistic回归分析结果显示,穿刺时间和手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素(均P<0.001)。原因可能为长时间血管穿刺操作可能增加血管内皮损伤概率,且穿刺时间较长意味着可能存在穿刺困难、需反复穿刺的情况,导致血管内皮损伤范围与程度增加,凝血因子激活并促进血小板黏附聚集[33-35];而手术时间延长可加重全身性应激与下肢静脉血液淤滞,导致DVT发生风险增高。优化手术流程、缩短操作时间可一定程度上提高穿刺成功率、减少穿刺损伤,对降低下肢DVT发生风险具有重要意义。
本研究结果显示,年龄、NIHSS评分、D-二聚体水平、穿刺时间及手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素。基于影响因素构建风险预测模型,结果显示,模型预测效能及临床实用性较好,AUC为0.916[95%CI(0.883,0.949)],DCA结果显示,在0.01~0.90阈值区间内具有净获益。
本研究存在一定局限性。1)为单中心回顾性研究,尽管已通过多因素分析控制混杂因素,但仍可能存在未测量的混杂偏倚。2)样本量相对有限,可能影响对部分危险因素效应量的精确估计。后续应进一步收集数据以验证本结论,同时应针对AIS病人探索机械取栓术后的精细化护理方案,推动基于风险预测模型的个体化下肢DVT预防实践,为临床决策提供更高级别的循证证据。
本研究结果显示,AIS病人机械取栓术后易发生下肢DVT,发生率高达28.89%,年龄、NIHSS评分、D-二聚体水平、穿刺时间及手术时间是AIS病人机械取栓术后发生下肢DVT的影响因素。基于影响因素构建的风险预测模型效能较好,可获得个体化的AIS病人机械取栓术后下肢DVT发生风险概率,便于医务人员在病人术后返室初期快速完成床旁精准筛查,及时识别高危病人,进而实施重点监护与针对性干预,实现从被动治疗到主动预防的护理模式转变,加快病人术后康复进程。

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doi: 10.12102/j.issn.1009-6493.2026.18.018
  • 接收时间:2026-01-07
  • 首发时间:2026-10-29
  • 出版时间:2026-09-25
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  • 收稿日期:2026-01-07
  • 修回日期:2026-08-10
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    1南方医科大学南方医院,广东 510515

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张晓梅,E-mail:
ZHANG Xiaomei, E-mail:
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https://castjournals.cast.org.cn/joweb/hlyj/CN/10.12102/j.issn.1009-6493.2026.18.018
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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