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. (31st December 2023)

• Record Type: Journal Article
• Authors: Zhang, Jing; Chen, Jia; Yang, Xiuqing; Han, Jing; Chen, Xiaofeng; Fan, Yueping; Zheng, Hui
• Is Part Of: Journal of obstetrics and gynaecology. Volume 43:Number 1(2023)
• Publication Date: 2023-12-31
• Subjects: Cervical cancer -- ovarian cancer -- machine learning -- prospective study -- venous thrombosis; Obstetrics -- Periodicals; Gynecology -- Periodicals; 618.05
• DOI: 10.1080/01443615.2023.2204162
• Languages: English
• ISSNs: 0144-3615

Abstract

Abstract: 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. Graphical Abstract: UF0001 Impact statement: What is already known on this subject? 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. What do the results of this study add? 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. What are the implications of these findings for clinical practice and/or further research? 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.