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中华神经创伤外科电子杂志

中华神经创伤外科电子杂志 ›› 2020, Vol. 06 ›› Issue (06) : 332 -337. doi: 10.3877/cma.j.issn.2095-9141.2020.06.003

所属专题： 文献；

临床研究

颅脑外伤去骨瓣减压术后并发脑积水的预测模型建立与验证
卓健伟1, 刘国东1, 曹鑫意1, 疏龙飞1, 王玉海1,†()   
  1. 1. 214044　无锡，解放军联勤保障部队第九〇四医院神经外科
  • 收稿日期:2020-07-28 出版日期:2020-12-15
  • 通信作者: 王玉海
  • 基金资助:
    江苏省卫健委重点科研项目(K2019018); 无锡市医学重点学科建设(ZDXK005)

Establishment and validation of a prediction model of post-traumatic hydrocephalus after decompressive craniotomy of traumatic brain injury

Jianwei Zhuo1, Guodong Liu1, Xinyi Cao1, Longfei Shu1, Yuhai Wang1,†()   

  1. 1. Department of Neurosurgery, The 904 Hospital of the Joint Service Support Force of the PLA, Wuxi 214044, China
  • Received:2020-07-28 Published:2020-12-15
  • Corresponding author: Yuhai Wang
  • About author:
    Corresponding author: Wang Yuhai, Email:
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引用本文：

卓健伟, 刘国东, 曹鑫意, 疏龙飞, 王玉海. 颅脑外伤去骨瓣减压术后并发脑积水的预测模型建立与验证[J]. 中华神经创伤外科电子杂志, 2020, 06(06): 332-337.

Jianwei Zhuo, Guodong Liu, Xinyi Cao, Longfei Shu, Yuhai Wang. Establishment and validation of a prediction model of post-traumatic hydrocephalus after decompressive craniotomy of traumatic brain injury[J]. Chinese Journal of Neurotraumatic Surgery(Electronic Edition), 2020, 06(06): 332-337.

目的

基于临床资料建立并验证创伤性颅脑损伤（TBI）去骨瓣减压（DC）术后并发脑积水（PTH）的预测模型。

方法

选取解放军联勤保障部队第九〇四医院神经外科自2010年1月至2019年12月诊治的符合纳入条件的451例TBI患者，随访6个月后根据PTH诊断标准分为PTH组（127例）和无PTH组（324例），分析与之可能相关的15项因素。采用最小绝对收缩和选择算子（LASSO）回归降低数据维度和筛选预测因子。纳入多因素Logistic回归分析后建立预测模型并制作列线图，受试者工作特征曲线（ROC）和校准曲线用于检测模型的区分度和拟合优度，决策曲线分析（DCA）评价模型的临床适用性。

结果

LASSO回归筛选出5个预测因子并用于构成模型。经过验证，模型的ROC曲线下面积为0.762。校准曲线显示预测概率与实际概率有很好的一致性。DCA表明模型在一定范围内的临床适用性。

结论

本研究建立一个PTH的预测模型，具有良好的区分度、拟合优度和临床适用性，有利于高危患者的识别和针对性随访干预。

关键词: 去骨瓣减压, 创伤性脑积水, 列线图, 预测模型
Objective

To establish and verify the prediction model of post-traumatic hydrocephalus (PTH) after decompressive craniotomy (DC) of traumatic brain injury (TBI) based on clinical data.

Methods

Four hundred and fifty-one eligible TBI cases admitted to Neurosurgery Department of the 904st Hospital of the Joint Service Support Force of the PLA from January 2010 to December 2019 were collected. After 6 months follow-up, according to the diagnostic criteria of PTH, the patients were divided into PTH group (n=127) and non-PTH group (n=324), and 15 factors related to PTH were analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the data dimension and filter the prediction factors. The multivariate logistic regression was used to establish the prediction model and constructe nomogram. Receiver operating characteristic (ROC) curve and calibration curve are respectively used to detect the discrimination and goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical applicability of the prediction model.

Results

Five predictors were screened out by LASSO regression and constituted a model. After verification, the area under the curve of ROC was 0.762. The calibration curve showed that the predicted probability was in good agreement with the actual probability. DCA showed the clinical applicability of the model in a certain range.

Conclusion

This study established a prediction model of PTH, which has good differentiation, goodness of fit and clinical applicability, and is conducive to the the identification of high-risk patients and targeted follow-up intervention.

Key words: Decompressive craniotomy, Post-traumatic hydrocephalus, Nomogram, Prediction model
表1 451例TBI患者DC术后发生PTH的单因素分析
项目 无PTH组(n=324) PTH组(n=127) χ2/Z值 P值
性别[例(%)]     0.125 0.723
  男 240(74.1) 92(72.4)    
  女 84(25.9) 35(27.6)    
年龄[岁，M(QR)] 48.0(26.0) 50.0(24.0) -1.460 0.144
开放性TBI[例(%)]     0.006 0.938
  是 45(13.9) 18(14.2)    
  否 279(86.1) 109(85.8)    
高血压[例(%)]     0.863 0.353
  有 59(18.2) 28(22.0)    
  无 265(81.8) 99(78.0)    
糖尿病[例(%)]     0.769 0.380
  有 14(4.3) 8(6.3)    
  无 310(95.7) 119(93.7)    
术前GCS评分[分，例(%)]     13.453 <0.001
  ≤6 179(55.2) 94(74.0)    
  >6 145(44.8) 33(26.0)    
术前Fisher分级[例(%)]     25.147 <0.001
  Ⅰ级 125(38.6) 27(21.3)    
  Ⅱ级 106(32.7) 33(26.0)    
  Ⅲ级 56(17.3) 36(28.3)    
  Ⅳ级 37(11.4) 31(24.4)    
脑室出血[例(%)]     6.492 0.011
  有 18(5.6) 16(12.6)    
  无 306(94.4) 111(87.4)    
脑中线移位[cm，例(%)]     1.258 0.533
  <0.5 88(27.2) 28(22.0)    
  0.5~1 142(43.8) 59(46.5)    
  >1 94(29.0) 40(31.5)    
去骨瓣减压[例(%)]     21.759 <0.001
  单侧 281(86.7) 86(67.7)    
  双侧 43(13.3) 41(32.3)    
骨窗疝[例(%)]     16.671 <0.001
  有 40(12.3) 36(28.3)    
  无 284(87.7) 91(71.7)    
颅内感染[例(%)]     12.159 <0.001
  有 56(17.3) 41(32.3)    
  无 268(82.7) 86(67.7)    
术后昏迷时间[d，例(%)]     54.707 <0.001
  <7 134(41.4) 16(12.6)    
  7~30 108(33.3) 35(27.6)    
  >30 82(25.3) 76(59.8)    
硬膜下积液[例(%)]     46.224 <0.001
  无 202(62.3) 48(37.8)    
  同侧 74(22.8) 24(18.9)    
  对侧 10(3.1) 6(4.7)    
  双侧 28(8.6) 32(25.2)    
  半球间 10(3.2) 17(13.4)    
去骨瓣面积[cm2，M(QR)] 81.4(32.4) 91.5(63.7) -3.518 <0.001
图1 基于LASSO回归的因素变量选择
图2 基于5个预测因子建立的预测TBI患者DC术后发生PTH的列线图
表2 TBI患者DC术后发生PTH的Logistic回归分析
变量 标准误 β Wald值 OR(95%CI) P值
术前Fisher分级          
  Ⅰ级       1(Ref)  
  Ⅱ级 0.315 0.168 0.284 1.183(0.638~2.196) 0.594
  Ⅲ级 0.337 0.525 2.416 1.690(0.872~3.274) 0.120
  Ⅳ级 0.356 0.966 7.370 2.628(1.308~5.280) 0.007
去骨瓣减压          
  单侧       1(Ref)  
  双侧 0.542 0.229 0.542 1.257(0.684~2.311) 0.462
骨窗疝          
  无       1(Ref)  
  有 0.300 0.401 1.790 1.493(0.830~2.686) 0.181
术后昏迷时间          
  <7 d       1(Ref)  
  7~30 d 0.345 0.636 3.408 1.890(0.961~3.714) 0.065
  >30 d 0.343 1.411 16.875 4.100(2.091~8.038) <0.001
硬膜下积液          
  无       1(Ref)  
  同侧 0.310 0.146 0.639 1.157(0.630~2.124) 0.639
  对侧 0.587 0.388 0.508 1.474(0.467~4.653) 0.508
  双侧 0.342 1.071 0.002 2.917(1.492~5.705) 0.002
  半球间 0.470 1.270 0.007 3.560(1.416~8.948) 0.007
常量 0.342 -2.511 0.000 0.081 <0.001
图3 预测模型的受试者工作特征曲线
图4 预测模型的校准曲线
图5 预测模型的决策曲线分析
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