目的:术前深静脉血栓形成(DVT)患者术后深静脉血栓形成(DVTp)的发生率明显,它有可能沉默,严重后果。因此,建立脊柱创伤患者术后DVTp风险预测模型非常重要.
方法:161例脊柱外伤患者术前DVT,入院后接受了脊柱手术,在2016年1月至2022年12月期间从我们医院收集。应用最小绝对收缩和选择算子(LASSO)结合多变量逻辑回归分析来选择变量以开发预测性逻辑回归模型。一个逻辑回归模型简单地用Caprini风险评分(模型A),而另一个模型不仅包含了以前筛选的变量,还包含了年龄变量(模型B)。使用灵敏度评估模型的能力,特异性,正预测值,负预测值,准确度,F1得分,和接收器工作特性(ROC)曲线。列线图简化并直观地呈现模型B,供临床医生和患者理解预测模型。使用决策曲线分析模型B的临床价值。
结果:本研究共纳入161例DVT患者。48例脊柱外伤患者术后发生DVTp,占入选患者总数的29.81%。模型A对脊柱创伤患者术后DVTp预测不足,训练数据集的ROCAUC值为0.595,测试数据集为0.593。通过LASSO回归和多变量logistic回归的应用,对七个危险因素进行了筛查:D-二聚体,血小板,高脂血症,血型,术前抗凝剂,脊髓损伤,下肢静脉曲张。模型B表现出卓越和一致的预测性能,训练数据集的ROCAUC值为0.809,测试数据集为0.773。根据校正曲线和判定曲线分析,模型B可以准确预测脊柱手术后发生DVTp的概率。列线图增强了模型B在图表和图形中的可解释性。
结论:总之,我们建立了一个logistic回归模型来准确预测脊柱创伤患者术后深静脉血栓形成的进展,利用D-二聚体,血小板,高脂血症,血型,术前抗凝剂,脊髓损伤,下肢静脉曲张,和年龄作为预测因素。所提出的模型优于简单基于CRS的逻辑回归模型。所提出的模型有可能帮助一线临床医生和患者识别和干预脊柱手术创伤患者的术后DVTp。
Patients with preoperative deep vein thrombosis (DVT) exhibit a notable incidence of postoperative deep vein thrombosis progression (DVTp), which bears a potential for silent, severe consequences. Consequently, the development of a predictive model for the risk of postoperative DVTp among spinal trauma patients is important.
Data of 161 spinal traumatic patients with preoperative DVT, who underwent spine surgery after admission, were collected from our hospital between January 2016 and December 2022. The least absolute shrinkage and selection operator (LASSO) combined with multivariable logistic regression analysis was applied to select variables for the development of the predictive logistic regression models. One logistic regression model was formulated simply with the Caprini risk score (Model A), while the other model incorporated not only the previously screened variables but also the age variable (Model B). The model\'s capability was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, and receiver operating characteristic (ROC) curve. Nomograms simplified and visually presented Model B for the clinicians and patients to understand the predictive model. The decision curve was used to analyze the clinical value of Model B.
A total of 161 DVT patients were enrolled in this
study. Postoperative DVTp occurred in 48 spinal trauma patients, accounting for 29.81% of the total patient enrolled. Model A inadequately predicted postoperative DVTp in spinal trauma patients, with ROC AUC values of 0.595 for the training dataset and 0.593 for the test dataset. Through the application of LASSO regression and multivariable logistic regression, a screening process was conducted for seven risk factors: D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities. Model B demonstrated superior and consistent predictive performance, with ROC AUC values of 0.809 for the training dataset and 0.773 for the test dataset. According to the calibration curves and decision curve analysis, Model B could accurately predict the probability of postoperative DVTp after spine surgery. The nomograms enhanced the interpretability of Model B in charts and graphs.
In summary, we established a logistic regression model for the accurate predicting of postoperative deep vein thrombosis progression in spinal trauma patients, utilizing D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities, and age as predictive factors. The proposed model outperformed a logistic regression model based simply on CRS. The proposed model has the potential to aid frontline clinicians and patients in identifying and intervening in postoperative DVTp among traumatic patients undergoing spinal surgery.