Analysis Of Related Factors And Prediction Model Of Intraoperative Bleeding In Epithelial Ovarian Cancer

Objective: Based on the clinical data of patients with epithelial ovarian cancer(EOC)undergoing surgery,a prediction model of the occurrence probability of intraoperative massive bleeding in EOC patients was established.Methods: The clinical data of patients admitted to Gynaecologic Oncology Department of the Affiliated Cancer Hospital of Guangxi Medical University and undergoing surgical treatment from January 2014 to January 2021 were included and retrospectively analyzed.Subjects were screened according to inclusion criteria and exclusion criteria.A total of 227 patients with epithelial ovarian cancer were included in the study.Whether intraoperative blood loss was more than 1000 ml was taken as the outcome index,and the patients were divided into non-massive bleeding group and massive bleeding group according to whether intraoperative massive bleeding occurred.Lasso regression was used to screen out the predictors of intraoperative massive bleeding for all objective variables,and univariate Logistic regression analysis was performed on the screened indicators,among which P < 0.05 was considered as the significant result.The variables with P < 0.05 were included in the multivariate Logistic regression analysis,and the risk factors of intraoperative massive bleeding were screened out by the stepwise elimination method.The predictive model of intraoperative massive bleeding was established by binary Logistic regression equation,and the predictive effect of the digital model was analyzed by receiver operating curve(ROC).Risk factors of intraoperative massive bleeding were incorporated into R software to construct the prediction model for the histogram,and the Boot-strap internal verification method was used for internal verification.The consistency index(C-index)was used to measure the differentiating ability of the prediction model in the verification queue.Calibration diagrams were also used to evaluate the predictive power of the model.Results: 1.Univariate analysis results showed that there were statistical differences in height,tumor diameter,preoperative serum albumin level,preoperative hemoglobin level,preoperative hematocellular volume value,preoperative serum fibrinogen level,preoperative serum D-dimer level,and bladder tumor resection(P < 0.05).2.Lasso regression analysis finally screened out variables related to intraoperative bleeding: height,imaging tumor diameter,serum albumin level,hematoctet value,fibrinogen level,D-dimer level,bladder tumor resection and intestinal tumor resection.3.Multivariate Logistic regression analysis results showed that: Height(OR=0.002,95%CI:0.000-0.348)was a protective factor for intraoperative massive bleeding(P < 0.05),while tumor diameter(OR=1.121,95%CI:1.048-1.200),D-dimer(OR=1.169,95%CI:1.067-1.281),bladder tumor resection(OR=8.498,95%CI: 1.5881-45.675),intestinal tumor resection(OR=1.889,95%CI:1.007-3.545)were independent risk factors for intraoperative massive bleeding in epithelial ovarian cancer(P < 0.05),both of which were highly correlated with intraoperative massive bleeding.Based on the above 5 risk factors of intraoperative massive bleeding in EOC patients,a histographic model was built to predict the risk of intraoperative massive bleeding.Internal verification showed that the predicted value was basically consistent with the actual value,and its C-index value was 0.767,which indicated that this model had good differentiation and accuracy.Conclusion: The height,imaging tumor diameter,D-dimer,bladder mass resection and bowel mass resection of EOC patients are all risk factors for intraoperative massive hemorrhage in EOC patients.The established prediction model of intraoperative massive hemorrhage in EOC patients has a good ability to predict the risk of intraoperative massive hemorrhage.