Novel risk prediction models, involving coagulation, thromboelastography, stress response, and immune function indicators, for deep vein thrombosis after radical resection of cervical cancer and ovarian cancer

This study aimed to investigate the predictive value of coagulation, thromboelastography, stress response, and immune function indicators for the occurrence of deep venous thrombosis (DVT) following radical resection of cervical cancer and ovarian cancer. We conducted a prospective, single-centre, case-control study that included 230 cervical cancer patients and 230 ovarian cancer patients. In the testing cohort, the final predictive model for cervical cancer patients was: Logit(P)=9.365–0.063(R-value)−0.112(K value) +0.386(α angle)+0.415(MA)+0.276(FIB)+0.423(D-D)+0.195(IL-6)+0.092(SOD). For ovarian cancer patients, the final model was: Logit(P)= −2.846–0.036(R-value)-0.157(K value) +0.426(α angle) +0.172(MA) +0.221(FIB)+0.375(CRP) −0.126(CD4⁺/CD8⁺). In the validation cohort, these models exhibited good predictive efficiency, with a false-positive rate of 12.5% and a false-negative rate of 2.9% for cervical cancer patients, and a false-positive rate of 14.3% and a false-negative rate of 0% for ovarian cancer patients. In conclusion, the risk prediction models developed in this study effectively improve the predictive accuracy of DVT following radical resection of cervical and ovarian cancer. <b>What is already known on this subject?</b> Nowadays, surgery is currently the primary treatment for gynecological malignant tumours. However, prior to surgery, these tumours often create a hypercoagulable state, which increases the likelihood of deep vein thrombosis (DVT) following the procedure. Reports have shown that the incidence of DVT after surgery for ovarian cancer is the highest among gynecological malignant tumours, ranging from 13.6% to 27%, with lower extremity DVT being the most common. The occurrence of embolic detachment poses the greatest risk of DVT and can lead to fatal pulmonary embolism. Identifying the factors that influence the occurrence of DVT after gynecological malignant tumour surgery is crucial in order to take necessary preventive measures for patients with high-risk factors and reduce the incidence of DVT. This is of great significance in ensuring the quality of surgery and improving the postoperative quality of life for patients. <b>What do the results of this study add?</b> This prospective, single-centre, case-control study was conducted to investigate the predictive value of coagulation, thromboelastography, stress response, and immune function indicators for the occurrence of deep venous thrombosis (DVT) following radical resection of cervical and ovarian cancer. This study included 230 cervical cancer patients and 230 ovarian cancer patients. Based on our findings, current risk prediction models that incorporate coagulation, thromboelastography, stress response, and immune function laboratory indicators have demonstrated the potential to improve the predictive accuracy of postoperative DVT in patients who have undergone radical resection of cervical and ovarian cancer. <b>What are the implications of these findings for clinical practice and/or further research?</b> Our study found that the final two regression models had a prediction accuracy of 87.9% and 87.4% for postoperative DVT in patients with cervical and ovarian cancer, respectively, which is a significant improvement. Furthermore, both models demonstrated high specificity of 100%. In addition, the models continued to perform well in terms of predictive efficiency, with a false positive rate of 12.5% and a false negative rate of 2.9% for cervical cancer patients and a false positive rate of 14.3% and a false negative rate of 0% for ovarian cancer patients. Our models are effective in predicting the occurrence of DVT in patients with cervical and ovarian cancer following resection.

本研究旨在探讨凝血功能、血栓弹力图（thromboelastography）、应激反应及免疫功能指标对宫颈癌与卵巢癌根治术后深静脉血栓形成（deep venous thrombosis, DVT）发生的预测价值。本研究采用前瞻性单中心病例对照研究设计，共纳入230例宫颈癌患者与230例卵巢癌患者。在测试队列中，宫颈癌患者的最终预测模型为：Logit(P)=9.365–0.063(R值)−0.112(K值)+0.386(α角)+0.415(最大振幅, MA)+0.276(纤维蛋白原, FIB)+0.423(D-二聚体, D-D)+0.195(白细胞介素6, IL-6)+0.092(超氧化物歧化酶, SOD)。针对卵巢癌患者的最终模型为：Logit(P)=−2.846–0.036(R值)−0.157(K值)+0.426(α角)+0.172(最大振幅, MA)+0.221(纤维蛋白原, FIB)+0.375(C反应蛋白, CRP)−0.126(CD4+/CD8+)。在验证队列中，上述模型展现出良好的预测效能：宫颈癌患者的假阳性率为12.5%、假阴性率为2.9%；卵巢癌患者的假阳性率为14.3%、假阴性率为0%。综上，本研究构建的风险预测模型可有效提升宫颈癌与卵巢癌根治术后DVT的预测准确度。

**本领域已有研究认知？**
目前手术仍是妇科恶性肿瘤的主要治疗手段。但术前肿瘤患者常处于高凝状态，会增加术后深静脉血栓形成的发生风险。有研究显示，卵巢癌术后DVT发生率在妇科恶性肿瘤中位居首位，为13.6%~27%，其中以下肢深静脉血栓最为常见。血栓脱落可引发严重的DVT相关风险，甚至可导致致死性肺栓塞。明确妇科恶性肿瘤术后DVT的影响因素，可为高危患者采取针对性预防措施、降低DVT发生率提供关键依据，这对保障手术质量、改善患者术后生活质量具有重要意义。

**本研究的新增发现？**
本项前瞻性单中心病例对照研究旨在探讨凝血功能、血栓弹力图、应激反应及免疫功能指标对宫颈癌与卵巢癌根治术后DVT发生的预测价值，共纳入230例宫颈癌患者与230例卵巢癌患者。基于本研究结果，纳入凝血功能、血栓弹力图、应激反应及免疫功能实验室指标的风险预测模型，具备提升宫颈癌与卵巢癌根治术后DVT预测准确度的潜力。

**本研究结果对临床实践及后续研究的启示？**
本研究显示，最终构建的两个回归模型分别对宫颈癌、卵巢癌患者术后DVT的预测准确度达87.9%与87.4%，预测效能提升显著。此外，两个模型的特异度均达100%。同时，模型在预测效能方面仍表现优异：宫颈癌患者的假阳性率为12.5%、假阴性率为2.9%；卵巢癌患者的假阳性率为14.3%、假阴性率为0%。本研究所构建的模型可有效预测宫颈癌与卵巢癌患者术后DVT的发生风险。