Article(id=1322218693300433004, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218660677140862, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202412018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733328000000, receivedDateStr=2024-12-05, revisedDate=1742227200000, revisedDateStr=2025-03-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1793171102117, onlineDateStr=2026-10-28, pubDate=1748534400000, pubDateStr=2025-05-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793171102117, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793171102117, creator=13701087609, updateTime=1793171102117, updator=13701087609, issue=Issue{id=1322218660677140862, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='10', pageStart='1029', pageEnd='1144', issueExtLink='null', onlineDate='null', pubDate='1748534400000', pubDateStr='2025-05-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793171094339, creator='13701087609', updateTime=1793325331065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322865576456868294, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218660677140862, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322865576456868295, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218660677140862, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1102, endPage=1109, ext={EN=ArticleExt(id=1322218694583890029, articleId=1322218693300433004, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Construction and verification of a nomogram prediction model for vascular plaque stability in patients with progressive cerebral infarction based on serum MCP-1, MCPIP1 combined with inflammatory factors, columnId=1320435174995772212, journalTitle=Journal of Army Medical University, columnName=Clinical Medicine, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct and validate a nomogram-based prediction model of vascular plaque stability in patients with progressive cerebral infarction based on serum monocyte chemotactic protein-1 (MCP-1), monocyte chemotactic protein-1 inducible protein 1 (MCPIP1) combined with inflammatory factors.

Methods‍

A retrospective cohort study was conducted on 200 patients with progressive cerebral infarction admitted to our department from January to December 2023. All of them were assigned into a modeling group, and were divided into a stable plaque subgroup and the unstable plaque subgroup according to results of carotid multilayer spiral CT angiography. Their general data, results of laboratory tests, and other clinical indicators were collected to identify the influencing factors for vascular plaque instability with single-factor and multifactor analyses. Then a nomogram model for predicting vascular plaque stability was constructed for patients with progressive cerebral infarction. Receiver operating characteristics (ROC) curve was plotted to evaluate the predictive performance of the nomogram model. Subsequently, in a ratio of 7∶3 between the cases in the modeling group and the validation group, another 86 patients with progressive cerebral infarction admitted to our department from January to June 2024 were enrolled and served as the validation group. Their clinical data were collected for external validation of the model.

Results‍

In the modeling group, there were 68 patients (34.00%) in the stable plaque subgroup and 132 patients (66.00%) in the unstable plaque subgroup. Univariate analysis showed that there were significant differences between the 2 subgroups in terms of age (65.31±6.74 vs 67.52±7.14 years, t=2.113), comorbid diabetes mellitus [35 (48.53%) vs 80 (60.61%) cases, Chi-square=7.182], MCP-1 (570.67±104.23 vs 693.94±128.45 pg/mL, t=6.836), MCPIP1 (2.93±0.58 vs 4.08±0.75 ng/mL, t=11.051), homocysteine (Hcy, 10.56±2.38 vs 16.04±3.54 μmol/L, t=11.491), C-reactive protein (CRP, 6.16±2.03 vs 8.05±2.67 mg/L, t=5.122) and TNF-ɑ (1.31±0.29 vs 1.79±0.47 ng/mL, t=7.696) (all P˂0.05). Multivariate analysis indicated that age (β=0.103, OR=1.109, 95%CI=1.012~1.215), comorbid diabetes (β=2.135, OR=8.461, 95%CI=1.866~38.353), Hcy (β=0.706, OR=2.026, 95%CI=1.550~2.650), MCP-1 (β=0.011, OR=1.011, 95%CI=1.004~1.018), MCPIP1 (β=1.928, OR=6.875, 95%CI=2.765~17.094), CRP (β=0.327, OR=1.387, 95%CI=1.022~1.883) and TNF-ɑ (β=1.491, OR=4.443, 95%CI=1.389~14.212) were independent influencing factors for vascular plaque instability in the patients with progressive cerebral infarction (all P<0.05). The modeling formula based on these factors was Logit (P)=0.103×(age)+2.135×(combined diabetes)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684. ROC curve analysis revealed that the area under curve (AUC) of the model in the modeling group was 0.956 (95%CI: 0.931~0.981, P<0.001), with a sensitivity of 0.841 and a specificity of 0.926, and the AUC value in validation group was 0.960 (95%CI=0.925~0.996, P<0.001).

Conclusion‍

Our nomogram prediction model has a good predictive performance for vascular plaque instability in patients with progressive stroke, and it can be used to identify high-risk patients for vascular plaque instability in clinical practice.

, authors=Yanda LI, Yan SONG, Yalun CHEN, Xu LI, Minheng WANG, Hui ZHANG, authorsList=Yanda LI, Yan SONG, Yalun CHEN, Xu LI, Minheng WANG, Hui ZHANG, authorCompany=null, correspAuthors=Yan SONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2025 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, 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=1322218696366469238, articleId=1322218693300433004, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=基于血清MCP-1、MCPIP1联合炎症因子的进展性脑梗死患者血管斑块稳定性列线图预测模型构建与验证, columnId=1322185541475488183, journalTitle=陆军军医大学学报, columnName=临床医学, runingTitle=null, highlight=null, articleAbstract=
目的

基于血清单核细胞趋化蛋白-1(monocyte chemotactic protein-1,MCP-1)、单核细胞趋化蛋白-1诱导蛋白1(monocyte chemotactic protein-1 inducible protein 1,MCPIP1)联合炎症因子构建进展性脑梗死患者血管斑块不稳定的列线图预测模型并验证。

方法

采用回顾性队列研究设计,选取2023年1-12月新乡医学院南阳市第二人民医院神经内科收治的进展性脑梗死的200例患者为建模组,并根据颈动脉多层螺旋CT血管成像检查结果分为血管斑块稳定组和血管斑块不稳定组。收集其一般资料、实验室指标等临床资料,采用单因素及多因素分析血管斑块不稳定的影响因素,并构建进展性脑梗死患者血管斑块稳定性的列线图预测模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型预测价值。另按照建模组与验证组7∶3的比例,选取2024年1-6月新乡医学院南阳市第二人民医院神经内科收治的进展性脑梗死患者86例为验证组,收集其临床资料对模型进行外部验证。

结果

建模组中血管斑块稳定组患者68例(34.00%),血管斑块不稳定组132例(66.00%)。单因素分析结果显示,2组患者的年龄[(65.31±6.74)岁 vs (67.52±7.14)岁,t=2.113)]、合并糖尿病[35(48.53) vs 80(60.61),χ2=7.182]、MCP-1[(570.67±104.23) pg/mL vs (693.94±128.45)pg/mL,t=6.836]、MCPIP1[(2.93±0.58)ng/mL vs. (4.08±0.75)ng/mL,t=11.051]、同型半胱氨酸(homocysteine,Hcy)[(10.56±2.38)μmol/L vs. (16.04±3.54)μmol/L,t=11.491]、C反应蛋白(C-reactive protein,CRP)[(6.16±2.03)mg/L vs. (8.05±2.67)mg/L,t=5.122]及肿瘤坏死因子-ɑ(tumor necrosis factor-ɑ,TNF-ɑ)[(1.31±0.29)ng/mL vs. (1.79±0.47)ng/mL,t=7.696]比较,差异具有统计学意义(均P˂0.05)。多因素分析结果显示,年龄(β=0.103,OR=1.109,95%CI=1.012~1.215)、合并糖尿病(β=2.135,OR=8.461,95%CI=1.866~38.353)、Hcy(β=0.706,OR=2.026,95%CI=1.550~2.650)、MCP-1(β=0.011,OR=1.011,95%CI=1.004~1.018)、MCPIP1(β=1.928,OR=6.875,95%CI=2.765~17.094)、CRP(β=0.327,OR=1.387,95%CI=1.022~1.883)及TNF-ɑ(β=1.491,OR=4.443,95%CI=1.389~14.212)均为进展性脑梗死患者血管斑块不稳定的独立影响因素(均P˂0.05)。根据多因素分析结果构建模型公式为Logit(P)=0.103×(年龄)+2.135×(合并糖尿病)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684。ROC曲线分析结果显示,建模组的ROC的曲线下面积(area under curve,AUC)为0.956(95%CI:0.931~0.981,P<0.001),敏感度为0.841,特异度为0.926。验证组的ROC的AUC为0.960(95%CI:0.925~0.996,P<0.001)。

结论

本研究构建的进展性脑卒中患者血管斑块不稳定的列线图预测模型预测性能较好,临床可据此识别高风险血管斑块不稳定的患者。

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宋彦,E-mail:
SONG Yan,E-mail:
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J Cell Physiol,2021,236(10):7211-7222., articleTitle=The molecular structure and role of CCL2 (MCP-1) and C-C chemokine receptor CCR2 in skeletal biology and diseases, refAbstract=null), Reference(id=1322218708857106635, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, doi=10.13201/j.issn.1001-1439.2022.10.006, pmid=null, pmcid=null, year=2022, volume=38, issue=10, pageStart=786, pageEnd=790, url=null, language=null, rfNumber=19, rfOrder=27, authorNames=黄成茜, 梁鹏, 郭福佳, journalName=临床心血管病杂志, refType=null, unstructuredReference=黄成茜,梁鹏,郭福佳,等. 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Medicine (Baltimore),2017,96(29):e7543., articleTitle=Development and validation of risk-stratification delirium prediction model for critically ill patients:a prospective,observational,single-center study, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322218696643293303, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, xref=1, ext=[AuthorCompanyExt(id=1322218696743956600, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, companyId=1322218696643293303, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Neurology,Nanyang Second People’s Hospital,Xinxiang Medical College,Nanyang,Henan,China), AuthorCompanyExt(id=1322218696752345209, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, companyId=1322218696643293303, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1新乡医学院南阳市第二人民医院神经内科,河南 南阳)])], figs=[ArticleFig(id=1322218701399634082, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=EN, label=null, caption=null, figureFileSmall=denKGTNFIrUr/wZfbuw0Vg==, figureFileBig=VZbAeLwlJY56A6nLnXxC7A==, tableContent=null), ArticleFig(id=1322218701500297379, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=CN, label=图1, caption=进展性脑梗死患者血管斑块不稳定的列线图预测模型, figureFileSmall=denKGTNFIrUr/wZfbuw0Vg==, figureFileBig=VZbAeLwlJY56A6nLnXxC7A==, tableContent=null), ArticleFig(id=1322218703358374052, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=EN, label=null, caption=null, figureFileSmall=dfahfi4oi+p0VADzPtxIVg==, figureFileBig=p9z8SWdakC/DjfHb0+x8Eg==, tableContent=null), ArticleFig(id=1322218703467425957, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=CN, label=图2, caption=进展性脑梗死患者颈动脉斑块不稳定列线图模型的ROC曲线

A:建模组; B:验证组

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A:建模组; B:验证组

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A:建模组; B:验证组

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指标血管斑块稳定组(n=68)血管斑块不稳定组(n=132)t/χ2P
性别0.1710.680
男性35(51.47)72(54.55)
女性33(48.53)60(45.45)
年龄/岁65.31±6.7467.52±7.142.1130.036
BMI/(kg/m2)22.62±4.3523.43±4.221.2730.205
NIHSS/分18.39±5.2119.42±6.131.1830.238
吸烟史0.9510.330
有24(35.29)56(42.42)
无44(64.71)76(57.58)
饮酒史0.5760.448
有39(57.35)83(62.88)
无29(42.65)49(37.12)
合并冠心病1.5890.208
有6(8.82)20(15.15)
无62(91.18)112(84.85)
合并高血压2.6630.103
有33(48.53)80(60.61)
无35(51.47)52(39.39)
合并糖尿病7.1820.007
有22(32.35)69(52.27)
无46(67.65)63(47.73)
TC/(mmol/L)4.92±0.955.04±1.110.7590.449
TG/(mmol/L)3.32±0.613.42±0.691.0090.314
LDL-C/(mmol/L)3.15±0.483.24±0.621.0460.297
HDL-C/(mmol/L)1.49±0.361.41±0.371.4620.145
Hcy/(μmol/L)10.56±2.3816.04±3.5411.491<0.001
MCP-1/(pg/mL)570.67±104.23693.94±128.456.836<0.001
MCPIP1/(ng/mL)2.93±0.584.08±0.7511.051<0.001
CRP/(mg/L)6.16±2.038.05±2.675.122<0.001
TNF-ɑ/(ng/mL)1.31±0.291.79±0.477.696<0.001
), ArticleFig(id=1322218704075600043, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=CN, label=表1, caption=

进展性脑梗死患者血管斑块不稳定的单因素分析 [x¯±s,例(%),n=200]

, figureFileSmall=null, figureFileBig=null, tableContent=
指标血管斑块稳定组(n=68)血管斑块不稳定组(n=132)t/χ2P
性别0.1710.680
男性35(51.47)72(54.55)
女性33(48.53)60(45.45)
年龄/岁65.31±6.7467.52±7.142.1130.036
BMI/(kg/m2)22.62±4.3523.43±4.221.2730.205
NIHSS/分18.39±5.2119.42±6.131.1830.238
吸烟史0.9510.330
有24(35.29)56(42.42)
无44(64.71)76(57.58)
饮酒史0.5760.448
有39(57.35)83(62.88)
无29(42.65)49(37.12)
合并冠心病1.5890.208
有6(8.82)20(15.15)
无62(91.18)112(84.85)
合并高血压2.6630.103
有33(48.53)80(60.61)
无35(51.47)52(39.39)
合并糖尿病7.1820.007
有22(32.35)69(52.27)
无46(67.65)63(47.73)
TC/(mmol/L)4.92±0.955.04±1.110.7590.449
TG/(mmol/L)3.32±0.613.42±0.691.0090.314
LDL-C/(mmol/L)3.15±0.483.24±0.621.0460.297
HDL-C/(mmol/L)1.49±0.361.41±0.371.4620.145
Hcy/(μmol/L)10.56±2.3816.04±3.5411.491<0.001
MCP-1/(pg/mL)570.67±104.23693.94±128.456.836<0.001
MCPIP1/(ng/mL)2.93±0.584.08±0.7511.051<0.001
CRP/(mg/L)6.16±2.038.05±2.675.122<0.001
TNF-ɑ/(ng/mL)1.31±0.291.79±0.477.696<0.001
), ArticleFig(id=1322218704230789292, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
血管斑块稳定性稳定=0,不稳定=1
年龄原值输入
合并糖尿病无=0,有=1
Hcy原值输入
MCP-1原值输入
MCPIP1原值输入
CRP原值输入
TNF-ɑ原值输入
), ArticleFig(id=1322218704377589933, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=CN, label=表2, caption=

变量赋值表

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
血管斑块稳定性稳定=0,不稳定=1
年龄原值输入
合并糖尿病无=0,有=1
Hcy原值输入
MCP-1原值输入
MCPIP1原值输入
CRP原值输入
TNF-ɑ原值输入
), ArticleFig(id=1322218704469864622, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
变量βS.E.WaldOR95%CIP
年龄0.1030.0474.9311.1091.012~1.2150.026
合并糖尿病2.1350.7717.6698.4611.866~38.3530.006
Hcy0.7060.13726.6312.0261.550~2.650<0.001
MCP-10.0110.00310.0601.0111.004~1.0180.002
MCPIP11.9280.46517.2106.8752.765~17.094<0.001
CRP0.3270.1564.4041.3871.022~1.8830.036
TNF-ɑ1.4910.5936.3174.4431.389~14.2120.012
), ArticleFig(id=1322218704562139311, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218693300433004, language=CN, label=表3, caption=

进展性脑梗死患者血管斑块不稳定的多因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量βS.E.WaldOR95%CIP
年龄0.1030.0474.9311.1091.012~1.2150.026
合并糖尿病2.1350.7717.6698.4611.866~38.3530.006
Hcy0.7060.13726.6312.0261.550~2.650<0.001
MCP-10.0110.00310.0601.0111.004~1.0180.002
MCPIP11.9280.46517.2106.8752.765~17.094<0.001
CRP0.3270.1564.4041.3871.022~1.8830.036
TNF-ɑ1.4910.5936.3174.4431.389~14.2120.012
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李彦达 , 宋彦 , 陈亚伦 , 李旭 , 王民珩 , 张辉
陆军军医大学学报 | 临床医学 2025,47(10): 1102-1109
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陆军军医大学学报 |临床医学 2025 , 47 (10) : 1102 -1109
基于血清MCP-1、MCPIP1联合炎症因子的进展性脑梗死患者血管斑块稳定性列线图预测模型构建与验证
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李彦达, 宋彦 , 陈亚伦, 李旭, 王民珩, 张辉
作者信息
  • 1新乡医学院南阳市第二人民医院神经内科,河南 南阳
通讯作者:
宋彦,E-mail:
SONG Yan,E-mail:
Construction and verification of a nomogram prediction model for vascular plaque stability in patients with progressive cerebral infarction based on serum MCP-1, MCPIP1 combined with inflammatory factors
Yanda LI, Yan SONG , Yalun CHEN, Xu LI, Minheng WANG, Hui ZHANG
Affiliations
  • 1Department of Neurology,Nanyang Second People’s Hospital,Xinxiang Medical College,Nanyang,Henan,China
出版时间: 2025-05-30 doi: 10.16016/j.2097-0927.202412018
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目的

基于血清单核细胞趋化蛋白-1(monocyte chemotactic protein-1,MCP-1)、单核细胞趋化蛋白-1诱导蛋白1(monocyte chemotactic protein-1 inducible protein 1,MCPIP1)联合炎症因子构建进展性脑梗死患者血管斑块不稳定的列线图预测模型并验证。

方法

采用回顾性队列研究设计,选取2023年1-12月新乡医学院南阳市第二人民医院神经内科收治的进展性脑梗死的200例患者为建模组,并根据颈动脉多层螺旋CT血管成像检查结果分为血管斑块稳定组和血管斑块不稳定组。收集其一般资料、实验室指标等临床资料,采用单因素及多因素分析血管斑块不稳定的影响因素,并构建进展性脑梗死患者血管斑块稳定性的列线图预测模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型预测价值。另按照建模组与验证组7∶3的比例,选取2024年1-6月新乡医学院南阳市第二人民医院神经内科收治的进展性脑梗死患者86例为验证组,收集其临床资料对模型进行外部验证。

结果

建模组中血管斑块稳定组患者68例(34.00%),血管斑块不稳定组132例(66.00%)。单因素分析结果显示,2组患者的年龄[(65.31±6.74)岁 vs (67.52±7.14)岁,t=2.113)]、合并糖尿病[35(48.53) vs 80(60.61),χ2=7.182]、MCP-1[(570.67±104.23) pg/mL vs (693.94±128.45)pg/mL,t=6.836]、MCPIP1[(2.93±0.58)ng/mL vs. (4.08±0.75)ng/mL,t=11.051]、同型半胱氨酸(homocysteine,Hcy)[(10.56±2.38)μmol/L vs. (16.04±3.54)μmol/L,t=11.491]、C反应蛋白(C-reactive protein,CRP)[(6.16±2.03)mg/L vs. (8.05±2.67)mg/L,t=5.122]及肿瘤坏死因子-ɑ(tumor necrosis factor-ɑ,TNF-ɑ)[(1.31±0.29)ng/mL vs. (1.79±0.47)ng/mL,t=7.696]比较,差异具有统计学意义(均P˂0.05)。多因素分析结果显示,年龄(β=0.103,OR=1.109,95%CI=1.012~1.215)、合并糖尿病(β=2.135,OR=8.461,95%CI=1.866~38.353)、Hcy(β=0.706,OR=2.026,95%CI=1.550~2.650)、MCP-1(β=0.011,OR=1.011,95%CI=1.004~1.018)、MCPIP1(β=1.928,OR=6.875,95%CI=2.765~17.094)、CRP(β=0.327,OR=1.387,95%CI=1.022~1.883)及TNF-ɑ(β=1.491,OR=4.443,95%CI=1.389~14.212)均为进展性脑梗死患者血管斑块不稳定的独立影响因素(均P˂0.05)。根据多因素分析结果构建模型公式为Logit(P)=0.103×(年龄)+2.135×(合并糖尿病)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684。ROC曲线分析结果显示,建模组的ROC的曲线下面积(area under curve,AUC)为0.956(95%CI:0.931~0.981,P<0.001),敏感度为0.841,特异度为0.926。验证组的ROC的AUC为0.960(95%CI:0.925~0.996,P<0.001)。

结论

本研究构建的进展性脑卒中患者血管斑块不稳定的列线图预测模型预测性能较好,临床可据此识别高风险血管斑块不稳定的患者。

‍脑梗死  /  动脉粥样硬化  /  斑块  /  炎症因子  /  列线图
Objective

To construct and validate a nomogram-based prediction model of vascular plaque stability in patients with progressive cerebral infarction based on serum monocyte chemotactic protein-1 (MCP-1), monocyte chemotactic protein-1 inducible protein 1 (MCPIP1) combined with inflammatory factors.

Methods‍

A retrospective cohort study was conducted on 200 patients with progressive cerebral infarction admitted to our department from January to December 2023. All of them were assigned into a modeling group, and were divided into a stable plaque subgroup and the unstable plaque subgroup according to results of carotid multilayer spiral CT angiography. Their general data, results of laboratory tests, and other clinical indicators were collected to identify the influencing factors for vascular plaque instability with single-factor and multifactor analyses. Then a nomogram model for predicting vascular plaque stability was constructed for patients with progressive cerebral infarction. Receiver operating characteristics (ROC) curve was plotted to evaluate the predictive performance of the nomogram model. Subsequently, in a ratio of 7∶3 between the cases in the modeling group and the validation group, another 86 patients with progressive cerebral infarction admitted to our department from January to June 2024 were enrolled and served as the validation group. Their clinical data were collected for external validation of the model.

Results‍

In the modeling group, there were 68 patients (34.00%) in the stable plaque subgroup and 132 patients (66.00%) in the unstable plaque subgroup. Univariate analysis showed that there were significant differences between the 2 subgroups in terms of age (65.31±6.74 vs 67.52±7.14 years, t=2.113), comorbid diabetes mellitus [35 (48.53%) vs 80 (60.61%) cases, Chi-square=7.182], MCP-1 (570.67±104.23 vs 693.94±128.45 pg/mL, t=6.836), MCPIP1 (2.93±0.58 vs 4.08±0.75 ng/mL, t=11.051), homocysteine (Hcy, 10.56±2.38 vs 16.04±3.54 μmol/L, t=11.491), C-reactive protein (CRP, 6.16±2.03 vs 8.05±2.67 mg/L, t=5.122) and TNF-ɑ (1.31±0.29 vs 1.79±0.47 ng/mL, t=7.696) (all P˂0.05). Multivariate analysis indicated that age (β=0.103, OR=1.109, 95%CI=1.012~1.215), comorbid diabetes (β=2.135, OR=8.461, 95%CI=1.866~38.353), Hcy (β=0.706, OR=2.026, 95%CI=1.550~2.650), MCP-1 (β=0.011, OR=1.011, 95%CI=1.004~1.018), MCPIP1 (β=1.928, OR=6.875, 95%CI=2.765~17.094), CRP (β=0.327, OR=1.387, 95%CI=1.022~1.883) and TNF-ɑ (β=1.491, OR=4.443, 95%CI=1.389~14.212) were independent influencing factors for vascular plaque instability in the patients with progressive cerebral infarction (all P<0.05). The modeling formula based on these factors was Logit (P)=0.103×(age)+2.135×(combined diabetes)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684. ROC curve analysis revealed that the area under curve (AUC) of the model in the modeling group was 0.956 (95%CI: 0.931~0.981, P<0.001), with a sensitivity of 0.841 and a specificity of 0.926, and the AUC value in validation group was 0.960 (95%CI=0.925~0.996, P<0.001).

Conclusion‍

Our nomogram prediction model has a good predictive performance for vascular plaque instability in patients with progressive stroke, and it can be used to identify high-risk patients for vascular plaque instability in clinical practice.

‍cerebral infarction  /  atherosclerosis  /  plaque  /  inflammation factors  /  nomogram
李彦达, 宋彦, 陈亚伦, 李旭, 王民珩, 张辉. 基于血清MCP-1、MCPIP1联合炎症因子的进展性脑梗死患者血管斑块稳定性列线图预测模型构建与验证. 陆军军医大学学报, 2025 , 47 (10) : 1102 -1109 . DOI: 10.16016/j.2097-0927.202412018
Yanda LI, Yan SONG, Yalun CHEN, Xu LI, Minheng WANG, Hui ZHANG. Construction and verification of a nomogram prediction model for vascular plaque stability in patients with progressive cerebral infarction based on serum MCP-1, MCPIP1 combined with inflammatory factors[J]. Journal of Army Medical University, 2025 , 47 (10) : 1102 -1109 . DOI: 10.16016/j.2097-0927.202412018
脑梗死是一类脑血管疾病,其主要特征为脑部缺血、缺氧导致的脑组织坏死或软化,又被称为缺血性脑卒中。研究表明,脑梗死的发病率及复发率均逐年上升,给患者家庭及社会造成了较大的经济负担[1]。进展性脑梗死是指在脑梗死临床治疗期间,患者原有病理状态继续恶化,且所致神经功能损伤症状在发病后6 h-1周内呈阶梯或进行性加重[2],具有较高的致残、致死率。颈动脉粥样硬化是脑梗死发病的重要病理基础,其稳定性与脑梗死病情程度及预后密切相关[3-4]。然而,目前临床对斑块稳定性的评估多依赖影像学检查,存在侵入性高、时效性差、难以动态监测等局限性。因此,探讨影响颈动脉血管斑块稳定性的因素对改善患者预后具有重要意义。核细胞趋化蛋白-1(monocyte chemotactic protein-1,MCP-1)是一种炎性细胞趋化因子,与其受体CCR2特异性结合产生的单核细胞趋化蛋白-1诱导蛋白1(monocyte chemotactic protein-1 inducible protein 1,MCPIP1)被认为是致颈部动脉粥样硬化形成及进展的2个关键因子[5-6]。此外,既往研究认为,动脉粥样硬化属于慢性低度炎症反应,炎症因子水平的变化可反映动脉粥样硬化的进展[7]。且目前研究对血清MCP-1、MCPIP1与炎症因子相结合的进展性脑卒中患者血管斑块不稳定性预测工具的构建有所欠缺。基于此,本研究拟基于血清MCP-1、MCPIP1联合炎症因子构建进展性脑卒中患者血管斑块不稳定的列线图预测模型,以期为临床识别高风险血管斑块不稳定的患者提供工具,为针对性的干预措施提供理论依据。
本研究为回顾性队列研究,设计参考GRIMES等[8]提出的临床研究分类法则。于医院信息管理系统采用随机抽样法选取2023年1-12月新乡医学院南阳市第二人民医院神经内科收治的符合纳排标准的进展性脑梗死患者200例为建模组,并根据颈动脉多层螺旋CT血管成像结果分为血管斑块稳定组和血管斑块不稳定组。血管斑块稳定性判定[9]:经颈动脉多层螺旋CT血管成像检查,测定斑块密度CT值,将斑块分为软斑块、混合斑块及钙化斑块,软斑块以脂质为主要成分,CT值50˂HU;混合斑块同时存在多种成分,CT值为50HU~120HU;钙化斑块以钙化成分为主,CT值>120HU;斑块类型为软斑块及混合斑块为血管斑块不稳定,斑块类型为钙化斑块为血管斑块稳定。另按照建模组与验证组例数7∶3的比例采用随机抽样法选取2024年1-6月新乡医学院南阳市第二人民医院神经内科收治的符合纳排标准的进展性脑梗死患者86例为验证组。纳入标准:①符合《中国急性缺血性卒中诊治指南2023》[10]中进展性脑梗死的相关诊断标准,经头部CT或MRI确诊;②经颈部彩色多普勒超声检查,显示存在颈动脉粥样硬化斑块;③首次发作,发病至入院时间˂48 h;④年龄≥18岁。排除标准:①合并其他严重器质性疾病;②合并恶性肿瘤;③既往脑梗死史;④合并免疫系统疾病;⑤凝血功能障碍、脑出血;⑥临床资料缺失。本研究已经新乡医学院南阳市第二人民医院医学伦理委员会批准(LY20240001)。
本研究结合既往经验及专家意见对预测因子进行筛选,并根据前期预实验中对患者的半结构式访谈结果,经过研究小组讨论后最终确立观察指标如下:①一般资料:性别、年龄、体质量指数(body mass index,BMI)、美国国立卫生研究院卒中量表(national institutes of health stroke scale,NIHSS)评分、吸烟史、饮酒史、合并冠心病、合并高血压、合并糖尿病;②实验室指标:血脂[甘油三酯(triglyceride,TG)、总胆固醇(total cholesterol,TC)、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)]、同型半胱氨酸(homocysteine,Hcy)、MCP-1、MCPIP1、炎症因子[C反应蛋白(C-reactive protein,CRP)、肿瘤坏死因子-ɑ(tumor necrosis factor-ɑ,TNF-ɑ)]。由研究者从医院信息管理系统中提取上述资料,并由2名研究者同时将数据录入电脑中,避免误差。
对符合正态分布的数据以x¯±s表示,定性资料用例(%)表示。采用SPSS 26.0统计软件进行数据分析,符合正态分布的定量资料组间比较采用独立样本t检验,定性资料组间比较采用χ2检验;采用Logistic回归分析进展性脑卒中患者血管斑块不稳定的影响因素;用R 3.5.3软件包和rms程序包构建列线图模型。采用受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)分析模型的预测价值,采用决策曲线分析(decision curve analysis,DCA)模型的临床适用性。以P<0.05为差异有统计学意义。
建模组中血管斑块稳定组患者68例(34.00%),血管斑块不稳定组132例(66.00%)。单因素分析结果显示,2组患者在年龄、合并糖尿病、血清Hcy、MCP-1、MCPIP1、CRP及TNF-ɑ等指标上差异具有统计学意义(P均˂0.05,表1)。
以进展性脑卒中患者的血管斑块稳定性为因变量,单因素分析中有统计学意义的变量为自变量,赋值后行二元Logistic回归分析(表2)。二元Logistic回归分析结果显示,年龄、合并糖尿病、血清Hcy、MCP-1、MCPIP1、CRP及TNF-ɑ均为进展性脑卒中患者血管斑块不稳定的独立影响因素(P均˂0.05,表3)。
根据多因素分析结果,构建模型公式为Logit(P)=0.103×(年龄)+2.135×(合并糖尿病)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684。以建模组患者数据为训练集,验证组患者数据为验证集,运用R语言软件绘制进展性脑梗死患者血管斑块不稳定的列线图预测模型,见图1。
Hosmer-Lemeshow检验结果显示:χ2=4.135,P=0.845。ROC曲线分析结果显示,建模组的ROC的AUC为0.956(95%CI=0.931~0.981,P<0.001),敏感度为0.841,特异度为0.926(图2A);验证组患者血管斑块稳定组34例(39.53%),血管斑块不稳定组52例(60.47%),其一般资料与建模组相比差异无统计学意义,验证组的ROC的AUC为0.960(95%CI=0.925~0.996,P<0.001),敏感度为0.904,特异度为0.912(图2B);建模组及验证组校准曲线与标准曲线较为贴合,具有较好的校准一致性(图3A、图3B)。DCA曲线分析显示,当预测概率阈值为0.10~1.00时,临床净收益率均大于“全干预”和“不干预”方案,提示模型临床适用性良好(图4A、4B)。
随着社会的发展,人们的生活方式和饮食习惯也显著改变,导致了脑血管疾病的高发,脑梗死作为常见的脑血管病之一,发病率也在逐年升高。动脉血管若存在斑块时,可导致血流量减少,血流速度较慢,继而影响靶器官正常功能,诱发缺血性心肌病或神经损伤[11]。当斑块进一步发展时,可出现破裂及脱落,进而堵塞血管,诱发急性脑梗死、急性心肌梗死等急性缺血性损伤,严重威胁患者的生命安全[12-13]。本研究中,建模组中血管斑块不稳定组132例,血管斑块不稳定的发生率为66.00%,处于较高的水平。因此,及时评估斑块稳定性,识别斑块稳定性的影响因素,并早期给予针对性的治疗手段,可有助于延缓斑块形成、改善斑块稳定性、减少斑块破裂及脱落,进而延缓进展性脑梗死患者的疾病进展及改善神经功能,提高预后[14]。故可认为本研究基于血清MCP-1、MCPIP1联合炎症因子构建进展性脑卒中患者血管斑块不稳定的列线图预测模型具有现实意义。
本研究结果显示,年龄、合并糖尿病、血清Hcy、MCP-1、MCPIP1、CRP及TNF-ɑ均为进展性脑卒中患者血管斑块不稳定的独立影响因素。其中,年龄越大的患者,机体正常功能衰退,血管调节能力衰退,对损伤的修复能力减弱,导致血管内皮长期受损,继而造成斑块加重,且高龄患者血管清除斑块能力较差,更易形成不稳定性斑块[15]。而合并糖尿病的患者在长期的高血糖刺激下,机体氧自由基生成增加,损伤血管内皮细胞,同时加剧了斑块内的炎性反应,斑块表面张力增加,导致斑块表面稳定性下降[16]。此外,Hcy是机体氨基酸代谢的中间产物,可降低一氧化氮合成酶表达,减少血管舒张因子一氧化氮产生,促使血栓素合成,增强血小板聚集及血管收缩,从而促进血管平滑肌增殖,增强血管内皮细胞损伤,并有较高血栓形成风险,可破坏斑块稳定性[17]。相关研究表明,MCP-1属于一种炎性细胞的趋化因子,当和其受体进行特异性结合后可生成MCPIP1,当血管内皮损伤后,MCP-1、MCPIP1可大量生成并促使炎性细胞黏附、迁移到内皮间隙的炎症反应区,促使颈部动脉粥样硬化发生及进展,降低斑块稳定性[18-19]。同时,CRP及TNF-ɑ均为典型的炎症因子,其水平升高可反应机体炎症反应增强,炎症反应的增强可促进基质金属蛋白酶的分泌、降解斑块纤维帽内的明胶,从而使得纤维帽厚度减小,增加了斑块破损的概率[20]。综上,建议临床重点关注高龄及合并糖尿病的进展性脑梗死患者,并采取针对性的治疗措施以降低患者的血清Hcy、MCP-1、MCPIP1、CRP及TNF-ɑ水平,维护血管斑块稳定性。
CHEN等[21]研究提到,当预测模型ROC的AUC为0.5~0.7、>0.7且≤0.9和>0.9时,分别判断为预测效果较低、中等和极好。本研究中,建模组及验证组的ROC的AUC分别为0.956及0.960(均P<0.001),提示本研究构建的风险预测模型对进展性脑梗死患者血管斑块稳定性的预测效果极好,且Hosmer-Lemeshow检验结果显示χ2=4.135,P=0.845,提示该模型预测进展性脑梗死患者血管斑块不稳定的概率与实际概率比较,无统计学差异,故本研究所构建模型拟合度较好,预测价值高,临床或可根据此模型评估进展性脑梗死患者血管斑块稳定性,选择针对性的干预措施进行治疗,以减轻疾病进展,改善预后。
综上所述,本研究基于血清MCP-1、MCPIP1联合炎症因子构建的进展性脑卒中患者血管斑块稳定性的列线图预测模型的预测效能极好,临床可据此辅助判断患者血管斑块稳定性,以选取个体化的治疗方案,提高预后。但本研究由于经费、时间等原因,存在单中心、小样本的局限性,具有一定的抽样误差和选择偏倚,后续研究可开展大样本的多中心联合的前瞻性研究以完善并推广本研究结果,增加研究结论的普适性。

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2025年第47卷第10期
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doi: 10.16016/j.2097-0927.202412018
  • 接收时间:2024-12-05
  • 首发时间:2026-10-28
  • 出版时间:2025-05-30
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  • 收稿日期:2024-12-05
  • 修回日期:2025-03-18
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    1新乡医学院南阳市第二人民医院神经内科,河南 南阳

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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
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