Establishment and validation of a nomogram model for predicting failed conversion of epidural labor analgesia to epidural surgical anesthesia in parturients undergoing intrapartum cesarean delivery

Abstract Objective The emergency conversion of epidural labor analgesia to intrapartum cesarean section anesthesia can lead to serious maternal and neonatal complication. This study aimed to establish a clinical predictive model to identify the risk of failed epidural conversion (FEC). Methods Clinical characteristics of 286 parturients who underwent conversion from epidural labor analgesia (ELA) in the Clinical Medical College of Yangzhou University were retrospectively collected. Univariate analysis and multivariate logistic regression were used to identify FEC risk factors. Risk factors were used to develop a predictive nomogram model. Area under the receiver-operating characteristic curve (AUC) calibration plots, and decision curve analysis (DCA) were used to assess the performance of the nomogram model. Results Independent predictors for FEC risk included ELA duration, non-obstetric anesthesiologist, visual analogue scores (VAS) within 2 h preceding cesarean section and size of the cervical orifice. The clinical prediction model was established based on the above four risk factors and showed superior predictive power both in training cohort (AUC = 0.876) and validation cohort (AUC = 0.839). The nomogram was well-calibrated. The decision curve analysis displayed that the FEC risk nomogram was clinically applicable. Conclusions The nomogram model can be used as a reliable and simple predictive tool for the identification of FEC, which will provide practical information for individualized treatment decisions.