目的 探讨多发伤患者深静脉血栓（deep vein thrombosis，DVT）形成的独立危险因素，构建风险预测模型并进行验证研究。 方法 回顾性选取2022年—2023年北京市某三级甲等医院创伤救治中心收治的425例多发伤患者为研究对象，根据超声结果分为DVT组和非DVT组，通过单因素和二元Logistic回归筛选变量，并通过R软件构建可视化列线图，应用Bootstrap法对模型性能进行内部验证和调整。 结果 多发伤患者DVT发生率为26.82%，Logistic回归分析显示年龄、合并骨盆髋臼骨折、合并下肢骨折、输血、24~48 h纤维蛋白降解产物>5 μg/ml是DVT发生的独立危险因素。基于以上因素构建预测模型并进行验证，AUC为0.777，Brier评分为0.049，灵敏度为0.728，特异度为0.704。验证集中预测DVT的AUC为0.749，Brier评分为0.070，灵敏度为0.553，特异度为0.826。 结论 该研究提供了多发伤患者DVT风险预测工具，指标可通过信息系统自动提取，可为早期筛查多发伤DVT高危患者提供依据。

Objective To explore the independent risk factors for deep vein thrombosis（DVT） in patients with multiple injuries，develop a risk prediction model for DVT and evaluate the effect. Methods By retrospective analysis method，425 multiple trauma patients from a tertiary hospital trauma center in Beijing from 2022 to 2023 were collected，and the patients were divided into DVT and non-DVT groups according to ultrasound results，and the variables were screened by univariate and binary logistic regression analysis. An visualized nomogram was constructed by R software，and the Bootstrap method was applied for the internal validation and model adjustment. Results The incidence of DVT in patients with multiple injuries in this study was as high as 26.82%. Logistic regression analysis showed that age，combined pelvic and acetabular fracture，combined lower extremity fracture，blood transfusion，and fibrinogen degradation products（FDP）>5 ug/ml within 24~48 h of admission were the independent risk factors for the occurrence of DVT. A risk prediction model for DVT in patients with multiple trauma was constructed and evaluated based on the above five factors，which predicted DVT with an AUC of 0.777，a Brier score of 0.049，a sensitivity of 0.728，and a specificity of 0.704. In the validation set，the AUC for predicting DVT was 0.749，the Brier score was0.070，the sensitivity was 0.553，and specificity was 0.826. Conclusion The model constructed in this study provides a simple method to calculate the DVT risk of patients with multiple trauma，and the indicators can be automatically extracted through the information system，providing a reference for early clinical screening of high-risk DVT patients with multiple trauma.