重症监护病房自发性脑出血患者深静脉血栓危险因素分析及预测模型构建  

Analysis of the risk factors of deep vein thrombosis in the patients with spontaneous intracerebral hemorrhage in ICU and construction of a predictive model

作　　者：张岩 许春阳 俞隼 温顺 叶宏伟 Zhang Yan;Xu Chunyang;Yu Sun;Wen Shun;Ye Hongwei(Department of ICU,the First People′s Hospital of Changshu City,Changshu Hospital Affiliated to Soochow University,Changshu 215500,China)

机构地区：[1]苏州大学附属常熟医院(常熟市第一人民医院)重症医学科,江苏常熟215500

出　　处：《中国急救医学》2024年第4期343-349,共7页Chinese Journal of Critical Care Medicine

摘　　要：目的探讨重症监护病房(ICU)自发性脑出血(sICH)患者发生深静脉血栓(DVT)的危险因素,并构建DVT预测模型。方法回顾性分析2019年1月至2023年1月常熟市第一人民医院ICU收治的357例sICH患者资料,以7∶3比例随机分为训练集(n=254)和验证集(n=103),依据其住院期间是否超声证实发生DVT分为DVT组(n=27)与非DVT组(n=227)。基于训练集数据,采用Logistic单因素及多因素回归分析法筛选DVT的独立危险因素,构建列线图预测模型,采用受试者工作特征(ROC)曲线和校准曲线评估列线图模型的区分度和拟合优度,决策曲线分析(DCA)评价预测模型的临床实用性。结果①共纳入254例训练集患者,其中男157例,女97例,DVT组共27例,发生率为10.63%;②单因素及多因素Logistic回归分析显示,机械通气(OR=3.574,P=0.018)、血肿体积(OR=4.280,P=0.002)、股静脉置管(OR=3.892,P=0.012)及血栓弹力图(TEG)中的凝血因子反应时间(R)(OR=0.337,P=0.021)是重症sICH患者发生DVT的独立危险因素;③基于筛选出的4项独立危险因素建立预测DVT发生风险的列线图模型,经过验证,该模型ROC曲线下面积(AUC)为0.813(95%CI 0.730~0.896),其校准曲线趋近于理想曲线;DCA提示预测模型具有临床有效性。结论基于Logistic回归分析构建的重症sICH患者DVT风险预测列线图模型对重症sICH患者发生DVT有较好预测效能,可以实现量化、个体化、可视化预测,为临床早期干预决策重症sICH的抗凝方案提供参考。Objective To explore the risk factors for deep vein thrombosis(DVT)in the patients with spontaneous intracerebral hemorrhage(sICH)in the intensive care unit(ICU)and to construct a predictive model for DVT.Methods Data of 357 patients with sICH admitted to ICU of the First People′s Hospital of Changshu City,Changshu Hospital Affiliated to Soochow University from January 2019 to January 2023 were retrospectively analyzed.The patients were randomly divided into a training set(n=254)and a validation set(n=103)in a ratio of 7∶3.Based on whether DVT was confirmed by ultrasound during hospitalization,the patients were divided into the DVT group and the non-DVT group.Logistic univariate and multivariate regression analysis were used to identify independent risk factors for DVT based on the training set data.A nomogram predictive model was constructed,and the discrimination and goodness-of-fit of the nomogram model were evaluated by using receiver operating characteristic(ROC)curves and calibration curves,respectively.Decision curve analysis(DCA)was used to evaluate the clinical applicability of the predictive model.Results①A total of 254 patients were included in the training set,including 157 males and 97 females.The DVT group had 27 cases with an incidence rate of 10.63%.②Univariate and multivariate Logistic regression analysis showed that mechanical ventilation(OR=3.574,P=0.018),hematoma volume(OR=4.280,P=0.002),catheterization of femoral vein(OR=3.892,P=0.012),and coagulation factor reaction time(R)in thromboelastography(OR=0.337,P=0.021)were independent risk factors for DVT in the patients with severe sICH.③A nomogram predictive model for estimating the risk of DVT occurrence was constructed based on the four independent risk factors.After validation,the area under the ROC curve(AUC)of the model was 0.813(95%CI 0.730-0.896),and the calibration curve approached the ideal curve.The DCA curve indicated that the predictive model had clinical utility.Conclusions The nomogram predictive model for DVT risk in the

关 键 词：自发性脑出血 深静脉血栓 危险因素 预测模型 机械通气 血肿体积 股静脉置管 凝血因子反应时间 

分 类 号：R743.34[医药卫生—神经病学与精神病学]