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中华神经创伤外科电子杂志 ›› 2023, Vol. 09 ›› Issue (05) : 277 -282. doi: 10.3877/cma.j.issn.2095-9141.2023.05.004

临床研究

急性颅脑损伤继发下肢静脉血栓的相关危险因素分析及预测模型构建
李飞翔, 段虎斌, 李晋虎, 吴昊, 王永红, 范益民()   
  1. 030001 太原,山西医科大学第一临床医学院神经外科
    030032 太原,山西白求恩医院神经外科
  • 收稿日期:2023-01-29 出版日期:2023-10-15
  • 通信作者: 范益民

Analysis of related risk factors and construction of prediction model for venous thrombosis of lower limbs secondary to acute traumatic brain injury

Feixiang Li, Hubin Duan, Jinhu Li, Hao Wu, Yonghong Wang, Yimin Fan()   

  1. Department of Neurosurgery, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
    Department of Neurosurgery, Bethune Hospital, Shanxi Medical University, Taiyuan 030032, China
  • Received:2023-01-29 Published:2023-10-15
  • Corresponding author: Yimin Fan
  • Supported by:
    Research Subject of Shanxi Provincial Health Commission(2019005)
引用本文:

李飞翔, 段虎斌, 李晋虎, 吴昊, 王永红, 范益民. 急性颅脑损伤继发下肢静脉血栓的相关危险因素分析及预测模型构建[J/OL]. 中华神经创伤外科电子杂志, 2023, 09(05): 277-282.

Feixiang Li, Hubin Duan, Jinhu Li, Hao Wu, Yonghong Wang, Yimin Fan. Analysis of related risk factors and construction of prediction model for venous thrombosis of lower limbs secondary to acute traumatic brain injury[J/OL]. Chinese Journal of Neurotraumatic Surgery(Electronic Edition), 2023, 09(05): 277-282.

目的

分析急性颅脑损伤(TBI)继发下肢静脉血栓(VET)的相关危险因素,并构建预测模型。

方法

回顾性选取2020年1月至2022年9月于山西白求恩医院神经外科就诊的200例急性TBI患者为研究对象,收集患者入院24 h的一般资料、影像资料、检验指标等,根据患者住院期间下肢彩超检查结果,分为VTE组和非VTE组,比较2组患者的各项指标,分析急性TBI患者发生下肢VET的危险因素,并建立风险预测模型,通过绘制ROC曲线评估其模型的预测价值。

结果

VTE组54例,非VTE组146例,2组患者在年龄、白细胞计数、中性粒细胞计数、血小板计数、入院GCS≤8分、下肢制动方面差异有统计学意义(P<0.05)。多因素Logistic回归分析显示,年龄、入院GCS≤8分、下肢制动为急性TBI患者发生下肢VET的独立危险因素(P<0.05)。利用危险因素构建列线图预测模型,采用ROC曲线分析显示:年龄、入院GCS≤8分、下肢制动诊断TBI患者发生下肢VET的曲线下面积分别为0.716(95%CI:0.642~0.791)、0.407(95%CI:0.341~0.500)、0.596(95%CI:0.501~0.691),该模型(以联合变量PRE表示)AUC值为0.770(95%CI:0.698~0.841),说明该模型对TBI患者发生下肢VET的预测效果较好。

结论

年龄、下肢制动和入院GCS≤8分是急性TBI患者发生下肢VET的独立危险因素,以此构建的风险预测模型有一定的预测价值。

Objective

To analyze the relevant risk factors of lower limb venous thrombosis (VET) secondary to acute traumatic brain injury (TBI) and establish a predictive model.

Methods

A retrospective study was conducted on 200 acute TBI patients who underwent Neurosurgery Department of Bethune Hospital, Shanxi Medical University January 2020 to September 2022. General information, imaging data, and test indicators of the patients were collected 24 h after admission. Based on the results of lower limb ultrasound examination during hospitalization, the patients were divided into VTE group (54 cases) and non VTE group (146 cases). The various indicators of the two groups of patients were compared, the independent risk factors for lower limb VET in TBI patients were analyzed, and the risk prediction model was established. The predictive value of the model was evaluated by drawing ROC curves.

Results

There were statistically significant differences in age, white blood cell count, neutrophil count, platelet count, admission GCS≤8 score, and lower limb immobilization between VTE group and the non VTE group (P<0.05). The multivariate Logistic regression analysis showed that age, admission GCS≤8 score, and lower limb immobilization were independent risk factors for lower limb VET in patients with acute TBI (P<0.05). A column chart prediction model was constructed using risk factors. ROC curve analysis showed that the area under the curve for lower limb VET in TBI patients diagnosed with age, admission GCS≤8 score, and lower limb immobilization were 0.716 (95%CI: 0.642-0.791), 0.407 (95%CI: 0.341-0.500), and 0.596 (95%CI: 0.501-0.691), respectively. The AUC value of the model (represented by the combined variable PRE) was 0.770 (95%CI: 0.698-0.841), indicating that the model has a good predictive effect on lower limb VET in TBI patients.

Conclusion

Age, lower limb immobilization, and admission GCS≤8 score are independent risk factors for lower limb VET in acute TBI patients, and the constructed risk prediction model has certain predictive value.

表1 2组患者的一般资料比较
Tab.1 Comparison of general information between two groups
项目 非VTE组(n=146) VTE组(n=54) t/U/χ2 P
年龄(岁,±s 47.08±16.40 58.43±12.13 -5.308 <0.001
男性[例(%)] 118(80.82) 40(74.07) 1.082 0.298
吸烟史[例(%)] 62(42.47) 23(42.59) <0.001 0.987
饮酒史[例(%)] 41(28.08) 14(25.93) 0.092 0.762
高血压[例(%)] 30(20.55) 12(22.22) 0.067 0.796
糖尿病[例(%)] 12(8.22) 7(12.96) 1.032 0.310
入院化验指标        
白细胞计数(×109/L,±s 14.15±6.42 11.71±4.49 3.003 0.003
中性粒计数(×109/L,±s 12.16±6.24 9.97±4.40 2.770 0.006
红细胞(×1012/L,±s 4.25±0.76 4.07±0.69 1.521 0.130
血红蛋白(g/L,±s 132.90±20.51 128.28±21.62 1.392 0.166
血小板计数[×109/L,M(P25,P75)] 209.00(181.00,255.50) 186.50(123.80,214.00) 2546.500 <0.001
ALT[IU/L,M(P25,P75)] 22.00(17.00,33.50) 20.00(15.40,30.00) 3201.000 0.072
血肌酐[μmol/L,M(P25,P75)] 71.60(65.30,80.40) 75.10(66.10,89.20) 3267.000 0.084
CRP[ng/L,M(P25,P75)] 25.00(7.30,76.80) 33.45(13.90,64.90) 3590.500 0.369
D-二聚体[ng/L,M(P25,P75)] 3398.00(633.50,15 092.00) 4264.50(1169.50,13 879.30) 3714.000 0.578
APTT[s,M(P25,P75)] 28.70(26.50,30.30) 27.60(26.40,29.70) 3410.500 0.233
白蛋白(g/L,±s 39.28±5.96 37.93±6.39 1.381 0.169
入院情况[例(%)]        
入院GCS评分(≤8分) 28(19.18) 20(37.74) 7.317 0.007
下肢气压泵 86(58.90) 33(62.26) 0.183 0.669
下肢制动 5(3.42) 12(22.22) 17.909 <0.001
手术 56(38.36) 16(29.63) 1.303 0.254
深静脉置管 59(40.41) 17(31.48) 1.334 0.248
表2 急性TBI患者发生下肢VTE的多因素Logistic回归分析
Tab.2 Multivariate logistic regression analysis of lower limb VTE in acute TBI patients
图1 急性TBI患者发生下肢VTE的风险预测模型
Fig.1 Risk prediction model of lower limb VTE in acute TBI patients
图2 急性TBI患者发生下肢VTE的风险预测模型的ROC曲线
Fig.2 ROC curve of risk prediction model of lower limb VTE in acute TBI patients
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