Zhang Cong, Chang Teding, Chen Deng, Luo Jialiu, Chen Shunyao, Zhang Peidong, Lin Zhiqiang, Li Hui
Department of Trauma Surgery, Emergency Surgery & Surgical Critical, Tongji Trauma Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China.
Risk Manag Healthc Policy. 2024 Dec 18;17:3187-3196. doi: 10.2147/RMHP.S487375. eCollection 2024.
Deep venous thrombosis (DVT), known to be a major factor in poor outcomes and death rates, is common after polytrauma with traumatic brain injury (TBI). In this study, a nomogram will be developed to predict the risk of DVT in polytrauma patients with TBI, since there is currently no specific and convenient diagnostic method.
A retrospective and observational trial was conducted between November 2021 and May 2023. The predictive model was created using a group of 349 polytrauma patients with TBI in a training set, with data collected between November 2021 and August 2022. A nomogram was presented after using multivariable logistic regression analysis to create the predictive model. Validation of the model was conducted internally. A separate group for validation included 298 patients seen consecutively between August 2022 and May 2023.
A total of 647 trauma patients were included in the study. Out of these, 349 individuals were part of the training group, while 298 were part of the validation group. Training cohorts reported 32.1% and validation cohorts reported 31.9% DVT. Age, Smoking, Injury Severity Score (ISS), Glasgow Coma Scale (GCS), D-dimer, Mechanical ventilation (MV) and Application of Vasoactive Drugs (AVD) comprised the individualized prediction nomogram. The model exhibited strong discrimination, achieving a C-index of 0.783 and a statistically insignificant result (P=0.216) following the Hosmer-Lemeshow test. Nomogram calibration plots and decision curve analysis showed the nomogram's utility in predicting DVT.
Our study characterized the incidence of DVT in polytrauma patients with TBI and further emphasized that it represented a substantial health concern, as evidenced by its frequency. Using this nomogram, it is possible to predict DVT in polytrauma patients with TBI based on demographics and clinical risk factors.
已知深静脉血栓形成(DVT)是导致不良预后和死亡率的主要因素,在合并创伤性脑损伤(TBI)的多发伤后很常见。在本研究中,将开发一种列线图来预测合并TBI的多发伤患者发生DVT的风险,因为目前尚无特异性和便捷的诊断方法。
于2021年11月至2023年5月进行了一项回顾性观察性试验。在一个训练集中,使用一组349例合并TBI的多发伤患者创建预测模型,数据收集于2021年11月至2022年8月。在使用多变量逻辑回归分析创建预测模型后,呈现了列线图。对模型进行了内部验证。一个单独的验证组包括2022年8月至2023年5月期间连续就诊的298例患者。
本研究共纳入647例创伤患者。其中,349例为训练组,298例为验证组。训练队列中DVT发生率为32.1%,验证队列中为31.9%。年龄、吸烟、损伤严重程度评分(ISS)、格拉斯哥昏迷量表(GCS)、D-二聚体、机械通气(MV)和血管活性药物应用(AVD)构成了个体化预测列线图。该模型表现出较强的区分能力,C指数为0.783,在Hosmer-Lemeshow检验后结果无统计学意义(P=0.216)。列线图校准图和决策曲线分析显示了列线图在预测DVT方面的效用。
我们的研究描述了合并TBI的多发伤患者中DVT的发生率,并进一步强调这是一个重大的健康问题,其发生频率就是证据。使用该列线图,可以根据人口统计学和临床风险因素预测合并TBI的多发伤患者发生DVT的情况。