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中华脑科疾病与康复杂志(电子版) ›› 2024, Vol. 14 ›› Issue (03) : 146 -153. doi: 10.3877/cma.j.issn.2095-123X.2024.03.004

临床研究

中重型颅脑创伤患者住院时间延长的危险因素分析及预测模型构建
王绅1, 王如海1,†(), 李春1, 杨震1, 孙菲琳1   
  1. 1. 236063　安徽阜阳，阜阳市第五人民医院神经外科
  • 收稿日期:2023-10-09 出版日期:2024-06-15
  • 通信作者: 王如海

Risk factors analysis and prediction model construction of prolonged length of stay in patients with moderate and severe traumatic brain injury

Shen Wang1, Ruhai Wang1,†(), Chun Li1, Zhen Yang1, Feilin Sun1   

  1. 1. Department of Neurosurgery, Fuyang Fifth People's Hospital, Fuyang 236063, China
  • Received:2023-10-09 Published:2024-06-15
  • Corresponding author: Ruhai Wang
  • Supported by:
    Research Project of the Health Commission of Fuyang City, Anhui Province(FY2023-019); Horizontal Medical Project of Fuyang Normal University(2024FYNUEY05)
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引用本文：

王绅, 王如海, 李春, 杨震, 孙菲琳. 中重型颅脑创伤患者住院时间延长的危险因素分析及预测模型构建[J]. 中华脑科疾病与康复杂志(电子版), 2024, 14(03): 146-153.

Shen Wang, Ruhai Wang, Chun Li, Zhen Yang, Feilin Sun. Risk factors analysis and prediction model construction of prolonged length of stay in patients with moderate and severe traumatic brain injury[J]. Chinese Journal of Brain Diseases and Rehabilitation(Electronic Edition), 2024, 14(03): 146-153.

目的

分析中重型颅脑创伤（TBI）患者住院时间延长（PLOS）的独立危险因素，并构建预测模型。

方法

回顾性分析阜阳市第五人民医院神经外科自2018年1月至2023年1月收治的533例中重型TBI患者的临床资料。按7∶3的比例随机分为训练集（374例）和验证集（159例），PLOS定义为住院时间≥28 d，按照是否发生PLOS将训练集分为PLOS组(60例)和非PLOS组（314例）。采用多因素Logistic回归分析研究PLOS发生的独立危险因素，基于上述独立危险因素采用R软件构建列线图预测模型，在训练集和验证集中分别绘制受试者工作特征（ROC）曲线、校正曲线和临床决策曲线分析（DCA），并进行Hosmer-Lemeshow拟合优度检验。

结果

PLOS组与非PLOS组的首次CT扫描时间、入院时GCS评分、体温、硬膜下血肿、血钾水平、血清总钙浓度、C-反应蛋白、甲状腺素、凝血酶原时间、活化部分凝血活酶时间、纤维蛋白原水平、D-二聚体及合并上消化道出血比较，差异有统计学意义（P<0.05）。多因素Logistic回归分析结果显示，体温>36.82℃、硬膜下血肿、血清总钙浓度≤1.97 mmol/L、D-二聚体>13.12 mg/L和合并上消化道出血是中重型TBI患者PLOS的独立危险因素。预测模型ROC曲线结果显示，训练集曲线下面积（AUC）为0.770（95%CI：0.699~0.840），验证集AUC为0.822（95%CI：0.754~0.889）。训练集和验证集的校正曲线均显示预测概率与实际概率趋于一致。DCA结果显示，列线图预测模型对中重型TBI患者PLOS发生风险有较好的预测效能。Hosmer-Lemeshow拟合优度检验中，训练集χ2=2.053，P=0.979；验证集χ2=4.566，P=0.803。

结论

体温>36.82℃、硬膜下血肿、血清总钙浓度≤1.97 mmol/L、D-二聚体>13.12 mg/L和合并上消化道出血是中重型TBI患者PLOS的独立危险因素，由此构建的预测模型具有良好的区分度、拟合优度和临床适用性，可为临床决策提供参考。

关键词: 中重型颅脑创伤, 住院时间延长, 预测模型
Objective

To analyze risk factors of prolonged length of stay (PLOS) in patients with moderate and severe traumatic brain injury (TBI), and to construct a prediction model.

Methods

A retrospective cohort study was performed to analyze the clinical data of 533 patients with moderate and severe TBI admitted to the Neurosurgery Department of Fuyang Fifth People's Hospital from January 2018 to January 2023, which were divided into a training set (n=374) and an validation set (n=159) according to the ratio of 7 to 3. Patients in the training set were grouped into two groups according to the hospital stay, namely PLOS group (hospital stay≥28 d) and non-PLOS group (hospital stay<28 d). Multivariate Logistic regression analyses were used to assess the risk factors of PLOS. The R software was used to establish a nomogram prediction model based on the above risk factors. The receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve analysis (DCA) were plotted in the training set and the validation set, and Hosmer-Lemeshow goodness-of-fit test was performed.

Results

The first CT scan time after injury, GCS score at admission, body temperature, subdural hematoma, plasma potassium level, serum calcium concentration, C-reactive protein, thyroxine, prothrombin time, activated partial thromboplastin time, fibrinogen level, D-dimer, and combined upper gastrointestinal hemorrhage were correlated with PLOS in patients with moderate and severe TBI. Logistic regression analysis showed that body temperature>36.82℃, subdural hematoma, serum calcium concentration≤1.97 mmol/L, D-dimer>13.12 mg/L and combined upper gastrointestinal hemorrhage were independent risk factors of PLOS in moderate and severe TBI patients. The ROC of the nomogram prediction model indicated that area under curve (AUC) of the training set was 0.770 (95%CI: 0.699-0.840) and AUC of the validation set was 0.822 (95%CI: 0.754-0.889). The calibration curve showed that the predicted probability was consistent with the actual situation in both the training set and validation set. DCA showed that the nomogram prediction model presented excellent performance in predicting PLOS. In Hosmer-Lemeshow goodness-of-fit test, χ2 value of the training set was 2.053 (P=0.979), with validation set of 4.566 (P=0.803).

Conclusion

Body temperature>36.82℃, subdural hematoma, serum calcium concentration≤1.97 mmol/L, D-dimer>13.12 mg/L and combined upper gastrointestinal hemorrhage were independent risk factors of PLOS in moderate and severe TBI patients. The nomogram prediction model based on these 5 predictive variables shows a good predictive performance, goodness-of-fit, and clinical applicability, which can provide a reference for clinical decision making.

Key words: Moderate and severe traumatic brain injury, Prolonged length of stay, Prediction model
表1 训练集中2组患者的临床资料比较
Tab.1 Comparison of clinical data between two groups of patients in the training set
项目 PLOS组（n=60） 非PLOS组（n=314） χ2/t/U值 P值
性别[例（%）]     0.002 0.966
男 39（65.0） 205（65.3）    
女 21（35.0） 109（34.7）    
年龄（岁，Mean±SD） 60.3±15.2 57.7±14.5 -1.481 0.139
受伤原因[例（%）]     3.802 0.149
交通伤 31（51.7） 123（39.2）    
摔跌伤 16（26.7） 90（28.7）    
其他 13（21.7） 101（32.2）    
基础疾病[例（%）]        
高血压 16（26.7） 75（23.9） 0.212 0.645
糖尿病 9（15.0） 38（12.1） 0.385 0.535
首次CT扫描时间[h，M（Q1，Q3）] 1.4（1.0，2.0） 2.0（1.3，2.6） -3.474 0.001
入院时GCS评分（分，Mean±SD） 8.7±3.6 10.3±2.3 -3.226 0.001
体温[℃，M（Q1，Q3）] 36.8（36.7，36.9） 36.8（36.7，36.8） -3.234 0.001
血压[mmHg，M（Q1，Q3）]        
收缩压 135.0（135.0，142.0） 135.0（135.0，142.0） -0.302 0.763
舒张压 85.0（85.0，90.0） 85.0（85.0，88.0） -1.690 0.091
血糖[mmol/L，M（Q1，Q3）] 7.0（7.0，7.5） 7.0（7.0，7.2） -1.833 0.067
实验室检查指标[M（Q1，Q3）]        
血小板计数（109/L） 192.5（142.8，243.8） 193.5（157.5，235.0） -0.395 0.693
血钾水平（mmol/L） 3.2（2.9，3.6） 3.5（3.2，3.8） -3.516 <0.001
血清总钙浓度（mmol/L） 2.1（1.9，2.2） 2.2（2.1，2.3） -3.713 <0.001
C-反应蛋白（mg/L） 1.1（1.1，1.1） 1.3（1.1，1.3） -4.068 <0.001
甲状腺素（μg/dL） 7.8（7.8，7.8） 7.81（7.81，7.81） -12.579 <0.001
凝血酶时间（s） 16.7（15.3，18.3） 16.8（15.4，18.3） -0.310 0.757
凝血酶原时间（s） 12.5（11.7，13.2） 12.0（11.2，12.8） -2.696 0.007
活化部分凝血活酶时间（s） 27.2（24.0，32.6） 25.6（23.6，28.9） -2.467 0.014
纤维蛋白原水平（g/L） 2.2（1.7，3.0） 2.5（2.0，3.1） -2.201 0.028
D-二聚体（mg/L） 19.1（4.7，36.4） 6.9（3.1，18.0） -3.505 <0.001
颅骨骨折[例（%）] 31（51.7） 160（51.0） 0.010 0.920
硬膜外血肿[例（%）] 29（48.3） 111（35.4） 3.625 0.057
硬膜下血肿[例（%）] 23（38.3） 56（17.8） 12.705 <0.001
脑内血肿[例（%）] 11（18.3） 35（11.1） 2.412 0.120
合并上消化道出血[例（%）] 10（16.7） 11（3.5） 16.470 <0.001
表2 中重型颅脑创伤患者住院时间延长的多因素Logistic分析
Tab.2 Multivariate Logistic regression analysis of prolonged length of stay in patients with moderate and severe traumatic brain injury
变量 偏回归系数 标准误 Wald's χ2值 OR值 95%CI P值
首次CT扫描时间 -0.025 0.083 0.091 0.975 0.830~1.147 0.763
入院时GCS评分 -0.034 0.070 0.238 0.967 0.843~1.108 0.626
体温 1.937 0.927 4.361 6.936 1.126~42.706 0.037
硬膜下血肿 1.124 0.357 9.933 3.078 1.530~6.194 0.002
血钾水平 -0.666 0.339 3.848 0.514 0.264~0.999 0.050
血清总钙浓度 -2.529 1.233 4.208 0.080 0.007~0.894 0.040
C-反应蛋白 0.170 0.304 0.312 1.185 0.653~2.152 0.576
甲状腺素 0.606 1.251 0.235 1.833 0.158~21.262 0.628
凝血酶原时间 -0.030 0.175 0.030 0.970 0.688~1.367 0.862
活化部分凝血活酶时间 0.015 0.034 0.203 1.015 0.951~1.084 0.653
纤维蛋白原水平 -0.160 0.207 0.600 0.852 0.568~1.278 0.439
D-二聚体 0.020 0.007 7.829 1.021 1.006~1.035 0.005
合并上消化道出血 2.003 0.541 13.716 7.411 2.568~21.392 <0.001
表3 预测PLOS的独立危险因素的ROC曲线分析结果
Tab.3 ROC curve analysis results for predicting independent risk factors of PLOS
危险因素 曲线下面积 敏感度 特异度 截断值 95%CI P值
体温 0.619 31.67 92.68 36.82℃ 0.567~0.668 0.003
硬膜下血肿 0.602 38.33 82.17 - 0.551~0.652 0.002
血清总钙浓度 0.651 38.33 91.72 1.97 mmol/L 0.601~0.699 0.001
D-二聚体 0.643 63.33 70.38 13.12 mg/L 0.592~0.691 0.001
合并上消化道出血 0.566 16.67 96.50 - 0.514~0.617 0.008
图1 中重型颅脑创伤患者住院时间延长风险预测列线图
Fig.1 Predictive nomogram model for the risk of prolonged length of stay in patients with moderate and severe traumatic brain injury
图2 列线图模型预测中重型颅脑创伤患者住院时间延长风险的ROC曲线A：训练集；B：验证集
Fig.2 ROC curve of predicting prolonged length of stay in patients with moderate and severe traumatic brain injury by the nomogram model
图3 列线图模型在训练集和验证集中的校正曲线A：训练集；B：验证集
Fig.3 Calibration curve for predicting the risk of prolonged length of stay in patients with moderate and severe traumatic brain injury by the nomogram model
图4 列线图模型评估中重型颅脑创伤患者住院时间延长风险的决策曲线分析A：训练集；B：验证集
Fig.4 Decision curve analysis for predicting the risk of prolonged length of stay in patients with moderate and severe traumatic brain injury by the nomogram model
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