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

LI Yanda; SONG Yan; CHEN Yalun; LI Xu; WANG Minheng; ZHANG Hui

doi:10.16016/j.2097-0927.202412018

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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

Clinical Medicine | 更新时间：2025-05-26

- 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
- Journal of Army Medical University Vol. 47, Issue 10, Pages: 1102-1109(2025)
- 作者机构：
  
  新乡医学院南阳市第二人民医院神经内科，河南南阳
- 作者简介：
  
  SONG Yan， E-mail： 1136811893@qq.com
- 基金信息：
  
  the Tackling Project of Medical Science and Technology Research Plan of Henan Province(LHGJ20230962);the Project of Science and Technology Development Plan of Nanyang(23KJGG160);the Joint Co-Construction Project of Medical Science and Technology Research Plan of Henan Province(LHGJ20191467)
- DOI：10.16016/j.2097-0927.202412018
  CLC：
- Received：05 December 2024，
  
  Revised：18 March 2025，
  
  Published：30 May 2025
- 稿件说明：
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李彦达，宋彦，陈亚伦，等.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［J］.陆军军医大学学报，2025，47（10）：1102-1109.DOI：10.16016/j.2097-0927.202412018.

LI Y D，SONG Y，CHEN Y L，et al.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］.J Army Med Univ，2025，47（10）：1102-1109.DOI：10.16016/j.2097-0927.202412018.
李彦达，宋彦，陈亚伦，等.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［J］.陆军军医大学学报，2025，47（10）：1102-1109.DOI：10.16016/j.2097-0927.202412018. DOI：

LI Y D，SONG Y，CHEN Y L，et al.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］.J Army Med Univ，2025，47（10）：1102-1109.DOI：10.16016/j.2097-0927.202412018. DOI：

摘要

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

（67.52±7.14）岁，

=2.113）］、合并糖尿病［35（48.53）

80（60.61），

=7.182］、MCP-1［（570.67±104.23） pg/mL

（693.94±128.45）pg/mL，

=6.836］、MCPIP1［（2.93±0.58）ng/mL

. （4.08±0.75）ng/mL，

=11.051］、同型半胱氨酸（homocysteine，Hcy）［（10.56±2.38）μmol/L vs. （16.04±3.54）μmol/L，

=11.491］、C反应蛋白（C-reactive protein，CRP）［（6.16±2.03）mg/L

. （8.05±2.67）mg/L，

=5.122］及肿瘤坏死因子-ɑ（tumor necrosis factor-ɑ，TNF-ɑ）［（1.31±0.29）ng/mL

. （1.79±0.47）ng/mL，

=7.696］比较，差异具有统计学意义（均

˂0.05）。多因素分析结果显示，年龄（

=0.103，

=1.109，95%

=1.012~1.215）、合并糖尿病（

=2.135，

=8.461，95%

=1.866~38.353）、Hcy（

=0.706，

=2.026，95%

=1.550~2.650）、MCP-1（

=0.011，

=1.011，95%

=1.004~1.018）、MCPIP1（

=1.928，

=6.875，95%

=2.765~17.094）、CRP（

=0.327，

=1.387，95%

=1.022~1.883）及TNF-ɑ（

=1.491，

=4.443，95%

=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

˂0.05）。根据多因素分析结果构建模型公式为Logit（

）=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%

：0.931~0.981，

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%

：0.925~0.996，

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。

Abstract

‍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 wer

e 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

s 67.52±7.14 years

=2.113)

comorbid diabetes mellitus [35 (48.53%)

80 (60.61%) cases

Chi-square

=7.182

]

MCP-1 (570.67±104.23

693.94±128.45 pg/mL

=6.836)

MCPIP1 (2.93±0.58

4.08±0.75 ng/mL

=11.051)

homocysteine (Hcy

10.56±2.38

16.04±3.54 μmol/L

=11.491)

C-reactive protein (CRP

6.16±2.03

8.05±2.67 mg/L

=5.122) and TNF-ɑ (1.31±0.29

1.79±0.47 ng/mL

=7.696) (all

˂0.05). Multivariate analysis indicated that age (

=0.103

=1.109

95%

=1.012~1.215)

comorbid diabetes (

=2.135

=8.461

95%

=1.866~38.353)

Hcy (

=0.706

=2.026

95%

=1.550~2.650)

MCP-1 (

=0.011

=1.011

95%

=1.004~1.018)

MCPIP1 (

=1.928

=6.875

95%

=2.765~17.094)

CRP (

=0.327

=1.387

95%

=1.022~1.883) and TNF-ɑ (

=1.491

=4.443

95%

=1.389~14.212) were independent influencing factors for vascular plaque instability in the patients with progressive cerebral infarction (all

0.05). The modeling formula based on these factors was Logit (

)=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%

: 0.931~0.981

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%

=0.925~0.996

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.

关键词

Keywords

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