机动车辆碰撞后患者创伤后应激障碍预测模型的构建与验证  

Construction and validation of a predictive model for post-traumatic stress disorder in patients after motor vehicle collisions

作　　者：景文春 Jing Wenchun(Department of Emergency,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China)

机构地区：[1]首都医科大学附属北京朝阳医院急诊科,北京100020

出　　处：《中华现代护理杂志》2024年第36期4951-4956,共6页Chinese Journal of Modern Nursing

摘　　要：目的构建并初步验证机动车辆碰撞(MVC)患者创伤后应激障碍(PTSD)预测模型。方法采用便利抽样法,选取2020年4月—2022年12月在首都医科大学附属北京朝阳医院急诊科就诊的279例MVC患者为研究对象。根据MVC患者交通事故后3个月是否发生PTSD,将患者分为PTSD组(n=96)和非PTSD组(n=183)。对两组患者的一般资料进行分析,采用二项Logistic回归分析MVC患者PTSD发病的影响因素,并据以构建多指标联合应用的回归预测/评估模型,采用受试者工作特征曲线下面积(AUC)预测诊断评估价值。纳入2023年1—3月首都医科大学附属北京朝阳医院急诊科收治的67例MVC患者以验证模型。结果二项Logistic回归分析结果显示,教育年限偏高、家庭关系不和睦、有颌面部损伤、有焦虑症病史、有抑郁症病史、事故过程中目睹死亡、自我感觉内疚和饮酒是患者MVC后出现PTSD的危险因素(P<0.05)。基于二项Logistic回归分析结果构建的8因子Log P模型,具有较高的预测效能,AUC为0.898(95%CI:0.808~0.974)。后续纳入的67例MVC患者样本对上述模型进行验证,验证结果及统计推断显示,差异比较:灵敏度、特异度及准确度的P值均>0.5,认可模型和验证结果具有相近的灵敏度、特异度和准确度。非劣效性检验P=0.010,等效性检验P=0.020,认可模型和验证结果等效。验证结果和真实样本的关联性及差异性检验显示,关联性检验P<0.01。优势性检验P=0.182,提示验证结果和真实样本差别不大。结论该预测模型具有良好的预测价值,且临床应用便捷,可为临床护理工作提供有益参考。ObjectiveTo construction and validation of a predictive model for post-traumatic stress disorder(PTSD)in patients after motor vehicle collisions(MVC).MethodsA convenience sampling method was used to select 279 MVC patients who visited the emergency department of Beijing Chaoyang Hospital,Capital Medical University,from April 2020 to December 2022.Patients were categorized into a PTSD group(n=96)and a non-PTSD group(n=183)based on whether they developed PTSD three months after the MVC.General data of both groups were analyzed,and binary Logistic regression analysis was conducted to identify influencing factors for PTSD onset in MVC patients.Based on these factors,a multi-indicator regression predictive/assessment model was developed,with its diagnostic value assessed using the area under the receiver operating characteristic curve(AUC).An additional 67 MVC patients admitted to the Emergency department from January to March 2023 were used to validate the model.ResultsBinary Logistic regression analysis identified high education level,poor family relationships,facial injuries,history of anxiety,history of depression,witnessing death during the accident,feelings of guilt,and alcohol use as significant risk factors for PTSD(P<0.05).The 8-factor Log P model developed based on binary Logistic regression analysis demonstrated strong predictive performance,with an AUC of 0.898[95%CI(0.808,0.974)].Validation with 67 additional MVC patients showed comparable sensitivity,specificity,and accuracy(P>0.5),indicating equivalence between the model and the validation results.Non-inferiority test yielded P=0.010,and equivalence test yielded P=0.020,confirming that the model and validation results are equivalent.Correlation tests between validation results and true samples revealed P<0.01.Superiority test P=0.182.ConclusionsThe predictive model demonstrates high predictive value and is convenient for clinical use,offering valuable guidance for clinical nursing practice.

关 键 词：创伤后应激障碍 预测模型 机动车辆 

分 类 号：R473.74[医药卫生—护理学]