解放军医学院学报2024，Vol.45Issue(7)：738-745,8.DOI:10.12435/j.issn.2095-5227.2024.094
骨盆骨折尿道损伤的危险因素分析及风险预测模型构建——基于1 358例患者的回顾性病例对照研究
Risk factors of urethral injury in pelvic fracture and prediction model constructing:A retrospective case-control study in 1 358 patients
刘凡 1孙新宇 2王力 3雷明星 2陈铭 2王万玲 4贾通宇 3宋勇 3马鑫3
作者信息
- 1. 解放军总医院卫勤部科研处,北京 100853
- 2. 解放军总医院第四医学中心骨科医学部,北京 100048||国家骨科与运动康复临床医学研究中心,北京 100048
- 3. 解放军总医院第三医学中心泌尿外科医学部,北京 100039
- 4. 解放军总医院医学创新研究部,北京 100853
- 折叠
摘要
Abstract
Background Pelvic fractures have high incidence in all blunt trauma cases presenting to the emergency department.However,associated urethral injuries were frequently ignored.Objective To investigate the risk factors associated with urethral injury in cases of pelvic fracture and establish a predictive model so as to give valuable guidance for clinicians in the diagnosis and treatment of urethral injuries resulting from pelvic fractures.Methods Clinical data about 1 358 patients with pelvic fractures hospitalized in the First Medical Center of Chinese PLA General Hospital from 2000 to 2020 were retrospectively analyzed.Rsik factors were analyzed using univariate and multivariate logistic regression.The data were divided into model training set and validation set according to the ratio of 7∶3.Urethral injury risk prediction models were developed by several machine learning models(including Gradient Boosting Machine,Support Vector Machine,Random Forest,XGBoost).The effectiveness of the model was evaluated using the AUC of the subject's work characteristic curve(ROC curve).Results To tally1 358 patients with pelvic fractures were included,with median age of 39(IQR:26-52)years,and 64.4%of them were male.The incidence of pelvic fracture urethral injury was 12.0%.Multivariate logistic regression showed that gender,department of consultation,Tiles staging of pelvic fracture,cause of injury,bipulmonary breath sounds,and neutrophils were independent risk factors for the occurrence of urethral injuries in patients with pelvic fracture(P＜0.05).Random forest model had the highest AUC of 0.955,and Gradient Boosting Machine,Support Vector Machine,and XGBoost model had AUCs of 0.936,0.940,and 0.953,respectively.Conclusion The occurrence of urethral injury among patients with pelvic fracture is associated with a variety of factors,including gender,department of consultation,Tiles typing of pelvic fracture,cause of injury,et al.Based on these independent risk factors,an early prediction model for urethral injury in patients with pelvic fracture has been developed and established using the random forest machine learning method with good predictive efficacy,which is helpful in clinically predicting and preventing the occurrence of combined urethral injury in patients with pelvic fracture at an early stage.关键词
骨盆骨折/尿道损伤/危险因素/预测模型/机器学习Key words
pelvic fracture/urethral injury/risk factors/prediction model/machine learning分类
医药卫生引用本文复制引用
刘凡,孙新宇,王力,雷明星,陈铭,王万玲,贾通宇,宋勇,马鑫..骨盆骨折尿道损伤的危险因素分析及风险预测模型构建——基于1 358例患者的回顾性病例对照研究[J].解放军医学院学报,2024,45(7):738-745,8.
