乳腺癌病人胸壁输液港发生静脉血栓的风险预测模型构建

    Establishment of a risk prediction model for venous thrombosis in breast cancer patients with chest wall venous ports

    • 摘要:
      目的 探讨乳腺癌病人胸壁完全植入式输液港(TIVAP)发生静脉血栓的危险因素,建立静脉血栓风险预测模型。
      方法 回顾性分析甲乳外科行TIVAP植入的乳腺癌病人共347例,根据是否发生静脉血栓分为非静脉血栓组288例和静脉血栓组59例。收集病人的一般资料和疾病相关的专科资料,采用单因素和多因素logistic回归方法分析TIVAP植入病人发生静脉血栓的危险因素,并根据结果建立静脉血栓的风险评估模型,绘制ROC曲线检验预测模型效能。
      结果 植入TIVAP的乳腺癌化疗病人静脉血栓发生率为17.00%(59/347)。新辅助化疗、肿瘤分期Ⅲ~Ⅳ期、导管尖端位于前肋1~2、D-二聚体升高均为乳腺癌化疗病人TIVAP植入后发生静脉血栓的独立危险因素(P < 0.05~P < 0.01)。ROC曲线分析显示,按照各风险因素构建TIVAP静脉血栓预测模型的AUC为0.873,灵敏度为0.849,特异度为0.822,准确率为95.6%。
      结论 新辅助化疗、肿瘤分期、导管尖端位置和D-二聚体水平均为乳腺癌病人TIVAP发生静脉血栓的独立影响因素,静脉血栓有较好预测效能。

       

      Abstract:
      Objective To understand the occurrence of venous thrombosis in chest wall totally implantable venous ports (TIVAP) in patients with breast cancer, and establish a risk prediction model.
      Methods A total of 347 breast cancer chemotherapy patients who underwent TIVAP implantation in the department of nail and breast surgery were retrospectively analyzed.According to whether venous thrombosis occurred or not, they were divided into the non-venous thrombosis group (288 cases) and the venous thrombosis group (59 cases).General data of patients and disease-related specialty data were collected, and the risk factors for venous thrombosis in patients with TIVAP implantation were analyzed using univariate and multivariate logistic regression methods.A risk prediction model for venous thrombosis was established based on the results, and ROC curves were plotted to test the efficacy of the prediction model.
      Results Among 347 breast cancer chemotherapy patients with TIVAP implantation, 59 cases of venous thrombosis occurred, with an incidence rate of 17.0%.The neoadjuvant chemotherapy, tumor stage Ⅲ-Ⅳ, the catheter tip located in the anterior rib 1-2, and increased D-dimer were risk factors for the occurrence of venous thrombosis after TIVAP implantation in breast cancer chemotherapy patients (P < 0.05 to P < 0.01).ROC curve analysis showed that the AUC for constructing the TIVAP venous thrombosis prediction model according to each risk factor was 0.873, sensitivity was 0.849, specificity was 0.822, and accuracy was 95.6%.
      Conclusions The neoadjuvant chemotherapy, tumor stage, catheter tip location, and increased D-dimer in breast cancer patients with TIVAP are risk factors for the occurrence of developing venous thrombosis, and venous thrombosis has a good predictive efficacy.

       

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