创伤性脑损伤患者发生经外周静脉置入中心静脉导管相关性上肢深静脉血栓预测模型的构建与验证  

Construction and validation of the prediction model for peripherally inserted central catheter-related upper extremity deep vein thrombosis in patients with traumatic brain injury

作　　者：邓哲 陈鑫[1] 罗婉嘉 邓文娟 黄勇强 刘翠玲[1,2] 夏建平[1,2] 张丽花[1,2] 周仙凡 陈元宜 Deng Zhe;Chen Xin;Luo Wanjia;Deng Wenjuan;Huang Yongqiang;Liu Cuiling;Xia Jianping;Zhang Lihua;Zhou Xianfan;Chen Yuanyi(Teaching and Research Section of Clinical Nursing,Xiangya Hospital,Central South University,Changsha 410008,China;Department of Neurosurgery,Xiangya Hospital,Central South University,Changsha 410008,China)

机构地区：[1]中南大学湘雅医院临床护理学教研室,长沙410008 [2]中南大学湘雅医院神经外科,长沙410008

出　　处：《中华创伤杂志》2024年第6期498-505,共8页Chinese Journal of Trauma

摘　　要：目的构建创伤性脑损伤(TBI)患者发生经外周静脉置入中心静脉导管相关性上肢深静脉血栓(PICC-UEDVT)的预测模型并对其进行验证。方法采用病例对照研究分析2019年1月至2021年12月中南大学湘雅医院收治的222例TBI患者的临床资料,其中男171例,女51例;年龄18~86岁[54.5(46.0,65.0)岁]。置管当天格拉斯哥昏迷评分(GCS)运动评分4.0(3.0,5.0)分。82例(36.9%)发生PICC-UEDVT。使用R语言软件将患者按7∶3随机拆分为训练集(156例,包括58例PICC-UEDVT)和验证集(66例,包括24例PICC-UEDVT)。对训练集和验证集的一般资料、静脉用药情况、导管情况、实验室指标进行基线比较。采用Lasso回归分析筛选变量,以是否诊断为PICC-UEDVT为结局变量,将回归系数非0的变量纳入多因素Logistic回归模型,按R语言软件的赤池信息量准则(AIC)筛选自变量,构建回归方程,并以此为基础建立预测TBI患者发生PICC-UEDVT的列线图模型。绘制训练集和验证集的受试者工作特征(ROC)曲线评价模型的区分度;采用Hosmer-Lemeshow(H-L)拟合优度检验及校准曲线评价模型的校准度;采用决策曲线分析(DCA)评价模型的临床实用性。结果训练集和验证集基线资料分析显示样本均衡性较好。Lasso回归分析筛选出5个预测变量,即置管当天GCS运动评分、置管当天Caprini评分、糖皮质激素使用情况、导管尖端位置、置管前D-二聚体(D-D)水平。多因素Logistic回归分析结果表明,置管当天Caprini评分(OR=1.20,95%CI 1.08,1.33)、糖皮质激素使用情况(OR=3.13,95%CI 0.99,10.46)、置管前D-D水平(OR=1.16,95%CI 1.07,1.33)是TBI患者发生PICC-UEDVT的独立危险因素。回归方程:Logit[P/(1-P)]=-2.56+0.18×"置管当天Caprini评分"+1.14×"糖皮质激素使用情况"+0.15×"置管前D-D水平"。在基于此建立的预测模型中,训练集和验证集的AUC分别为0.73(95%CI 0.65,0.81)、0.77(95%CI 0.65,0.87)。H-L拟合优度检验显示,训练集χ^(2)=3.Objective To construct a prediction model for peripherally inserted central catheter-related upper extremity deep vein thrombosis(PICC-UEDVT)in patients with traumatic brain injury(TBI)and validate its effectiveness.Methods A case-control study was conducted on the clinical data of 222 TBI patients admitted to Xiangya Hospital of Central South University from January 2019 to December 2021,including 171 males and 51 females,aged 18-86 years[54.5(46.0,65.0)years].Glasgow coma scale(GCS)motor score was 4.0(3.0,5.0)points on the day of catheterization.A total of 82 patients(36.9%)had PICC-UEDVT.The patients were randomly divided with a ratio of 7∶3 into training set(n=156,including 58 with PICC-UEDVT)and validation set(n=66,including 24 with PICC-UEDVT)using R programming language.The baseline data of general information,intravenous medication,catheterization,and laboratory indices were compared between the training set and the validation set.Lasso regression analysis was employed to identify those variables,with the diagnosis of PICC-UEDVT as the outcome variable.Variables with non-zero regression coefficients were included in a multifactorial Logistic regression model and independent variables were selected based on the Akaike Information Criterion(AIC)of R programming language.The regression equation was constructed,based on which,the predictive nomogram model was constructed for PICC-UEDVT in TBI patients.Receiver operating characteristic(ROC)curves for the training set and validation set were plotted and the discriminability of the model was assessed.The calibration of the model was evaluated using the Hosmer-Lemeshow(H-L)goodness-of-fit test and calibration curves and the clinical practicality of the model was assessed with decision curve analysis(DCA).Results The baseline analysis of both the training set and the validation set demonstrated a well-balanced sample distribution.Through Lasso regression analysis,5 prediction variables were identified:GCS motor score on the day of catheterization,Caprini score o

关 键 词：脑损伤 静脉血栓形成 导管插入术 外周 列线图 模型 

分 类 号：R651.15[医药卫生—外科学]