Zhang Hao-Ran, Xu Ming-You, Yang Xiong-Gang, Wang Feng, Zhang Hao, Yang Li, Qiao Rui-Qi, Li Ji-Kai, Zhao Yun-Long, Zhang Jing-Yu, Hu Yong-Cheng
Department of Bone Tumor, Tianjin Hospital, Tianjin, China.
Department of Orthopedics, Huashan Hospital, Fudan University, Shanghai, China.
Front Oncol. 2021 Jun 24;11:629823. doi: 10.3389/fonc.2021.629823. eCollection 2021.
Venous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.
We retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.
In the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.
The prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.
静脉血栓栓塞可分为深静脉血栓形成和肺栓塞。这些疾病是影响患者临床预后的主要因素，可导致患者死亡。遗憾的是，关于脊柱转移性骨病变手术后静脉血栓栓塞危险因素的文献很少，且尚未建立预测模型。
我们回顾性分析了2009年至2019年在我院接受转移性脊柱肿瘤手术的411例癌症患者。本研究的结局变量是术后90天内发生的静脉血栓栓塞。为了确定静脉血栓栓塞的危险因素，首先进行单因素逻辑回归分析，然后将P值小于0.2的显著变量纳入多因素逻辑回归分析。最后，使用独立危险因素建立列线图模型。
在多因素逻辑回归模型中，进一步筛选出静脉血栓栓塞的四个独立危险因素，包括术前Frankel评分（OR=2.68，95%CI 1.78-4.04，P=0.001）、输血（OR=3.11，95%CI 1.61-6.02，P=0.041）、Charlson合并症指数（OR=2.01，95%CI 1.27-3.17，P=0.013；OR=2.29，95%CI 1.25-4.20，P=0.017）和手术时间（OR=1.36，95%CI 1.14-1.63，P=0.001）。基于多因素逻辑回归模型筛选出的四个独立影响因素，建立了列线图预测模型。训练样本和验证样本均显示，列线图的预测概率与实际情况具有很强的相关性。
我们团队开发的术后VTE预测模型为临床医生提供了一种简单的方法，可用于在床边计算患者的VTE风险，并可帮助临床医生就是否使用干预措施做出循证判断。在临床实践中，这种预测模型的简便性具有很大的实用价值。
