Objective:To explore the risk factors of failure of vaginal trial delivery and transfer to cesarean section in parturients with combined spinal-epidural analgesia,and to construct its nomogram prediction model.Methods:A total of 512 parturients who un-derwent combined spinal-epidural analgesia were included.According to the final delivery method,the patients were divided into vaginal delivery group(n=483)and cesarean section group(n=29).Multivariate Logistic regression analysis was used to establish the inde-pendent risk factors for the failure of vaginal trial production and conversion to cesarean section in parturients undergoing combined spi-nal-epidural analgesia.The nomogram prediction model was constructed,and the receiver operating characteristic(ROC)curve was used to verify the prediction efficiency of the model.Results:There were significant differences in age,BMI at delivery,amniotic fluid indexing,eclampsia and preeclampsia,gestational diabetes mellitus/gestational diabetes mellitus,lower uterine segment muscle wall thickness,Bishop score and HAD score between the two groups(P＜0.05).Logistic regression analysis showed that advanced age,high BMI at delivery,eclampsia and preeclampsia,low Bishop score and high HAD score were independent risk factors for conversion to ce-sarean section after failure of vaginal trial production in combined spinal-epidural analgesia(P＜0.05).ROC curve analysis showed that the nomogram model constructed based on the independent risk factors mentioned above predicted an AUC of 0.835(95％CI:0.800～0.866)for cesarean section during delivery with lumbar epidural analgesia,with a sensitivity and specificity of 89.66％and 67.08％,respectively.Conclusion:Advanced age,high BMI at delivery,combined with eclampsia and preeclampsia,low Bishop score and high HAD score are independent risk factors for conversion to cesarean section after failure of vaginal trial production in parturients undergoing combined spinal-epidural analgesia.The nomogram model constructed based on the above factors has good predictive value.