交通事故相关创伤性颅脑损伤患者发生重度抑郁的影响因素及其风险预测列线图模型构建  

Influencing Factors of Severe Depression in Patients with Traffic Accident-Related Traumatic Brain Injury and Construction of the Nomogram Model for Predicting Its Risk

作　　者：周铭铭 肖江涛 王舒琪 张晓莉[1] 金雪娇 王旭生[1] ZHOU Mingming;XIAO Jiangtao;WANG Shuqi;ZHANG Xiaoli;JIN Xuejiao;WANG Xusheng(Department of Neurology,the Second Affiliated Hospital of Xinxiang Medical University,Xinxiang 453000,China;Department of Radiology,Xiamen Tong'an District Maternity and Child Care Hospital,Xiamen 361000,China)

机构地区：[1]新乡医学院第二附属医院神经内科二病区,河南省新乡市453000 [2]福建省厦门市同安区妇幼保健院放射科,361000

出　　处：《实用心脑肺血管病杂志》2025年第5期59-64,共6页Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease

基　　金：河南省精神心理疾病临床医学研究中心项目(2020-zxkfkt-008);河南省生物精神病学重点实验室开放课题(ZDSYS2020008);河南省医学科技攻关计划联合共建和软科学项目(LHGJ20240501,LHGJ20240505)。

摘　　要：目的探讨交通事故相关创伤性颅脑损伤(TBI)患者发生重度抑郁的影响因素,并构建其风险预测列线图模型。方法选取2022年7月—2024年6月新乡医学院第二附属医院神经内科收治的交通事故相关TBI患者136例作为研究对象,根据入院时重度抑郁发生情况将患者分为重度抑郁组66例和正常组70例。收集患者的临床资料,采用多因素Logistic回归分析探讨交通事故相关TBI患者发生重度抑郁的影响因素。采用R 4.1.1软件包及rms程序包构建交通事故相关TBI患者发生重度抑郁的风险预测列线图模型;采用Hosmer-Lemeshow拟合优度检验评价该列线图模型的拟合程度;采用Bootstrap法重复抽样1000次以对该列线图模型进行内部验证,并计算一致性指数(CI);绘制ROC曲线以分析该列线图模型的预测价值。结果两组损伤脑叶数目、额叶损伤发生率,头颈部、面部、胸部、腹部或盆腔、四肢或骨盆、体表简明损伤量表(AIS)评分,损伤严重程度评分(ISS)、合并重度颅外损伤者占比、改良Barthel指数(mBI)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,额叶损伤、胸部AIS评分、四肢或骨盆AIS评分、ISS、合并重度颅外损伤是交通事故相关TBI患者发生重度抑郁的独立影响因素(P<0.05)。基于多因素Logistic回归分析结果构建交通事故相关TBI患者发生重度抑郁的风险预测列线图模型。Hosmer-Lemeshow拟合优度检验结果显示,该列线图模型的拟合程度较好(χ^(2)=6.205,P=0.624)。内部验证结果显示,该列线图模型的CI为0.923〔95%CI(0.879~0.968)〕。ROC曲线分析结果显示,该列线图模型预测交通事故相关TBI患者发生重度抑郁的AUC为0.923〔95%CI(0.879~0.968)〕。结论额叶损伤、胸部AIS评分、四肢或骨盆AIS评分、ISS、合并重度颅外损伤是交通事故相关TBI患者发生重度抑郁的影响因素,基于上述因素构建的列线图模型具有较高�Objective To discuss the influencing factors of severe depression in patients with traffic accident-related traumatic brain injury(TBI)and construct nomogram model for predicting Its risk.Methods A total of 136 patients with traffic accident-related TBI admitted to the Second Affiliated Hospital of Xinxiang Medical University from July 2022 to June 2024 were selected as the study objects.The patients were divided into the severe depression group with 66 cases and the control group with 70 cases according to the incidence of severe depression at admission.The clinical data of patients were collected,and multivariate Logistic regression analysis was used to explore the influencing factors of severe depression in patients with traffic accident-related TBI.R 4.1.1 software and rms program package were used to construct the nomogram model for predicting severe depression in patients with traffic accident-related TBI,the Hosmer-Lemeshow goodness-of-fit test was used to evaluate the fitting degree of the nomogram model.The Bootstrap method was used to repeat sampling for 1000 times to perform internal validation of the nomogram model,and the consistency index(CI)was calculated.The ROC curve was drawn to evaluate the predictive value of the nomogram model.Results There were statistically significant differences of number of injured brain lobes,incidence of frontal lobe injury,Abbreviated Injury Scale(AIS)score of head and neck,face,chest,abdomen or pelvis,limbs or pelvis and body surface,Injury Severity Score(ISS),proportion of patients complicated with severe extracranial injury,modified Barthel Index(mBI)between the two groups(P<0.05).Multivariate Logistic regression analysis results showed that,frontal lobe injury,AIS score of neck,limbs or pelvis,ISS,severe extracranial injury were independent influencing factors of severe depression in patients with traffic accident-related TBI(P<0.05).The nomogram model for predicting severe depression in traffic accident-related TBI patients was established based on the results of

关 键 词：颅脑损伤 抑郁 交通事故 影响因素分析 列线图 

分 类 号：R651[医药卫生—外科学]