The Intelligent Regulation Model of Oxytocin for Women Having Vaginal Birth After Cesarean Section

Objective: To build an intelligent regulation model of oxytocin (OT) for women having a vaginal birth after cesarean section (VBAC) and further explore the intelligent and precise regulation of the scheme of the OT medication at delivery. Methods: A cross-sectional study design was used by collecting the data of the VBAC parturitions who delivered in the Obstetrics Department of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to May 2020 and used the OT during the labor process. The multiple linear regression was used to analyze the modeling variables from the electronic medical records and the variables such as fetal heart rate (FHR) and uterine contraction (UC) frequency extracted from the cardiotocography. The OT drip speed predictive model was established based on the XGBoost algorithm and was compared using the logistic regression model and traditional decision tree. The data set was divided into the training set and test set at the 8:2 ratio, and the predictive performance of the model was evaluated for the accuracy, precision, recall rate, and F1 score. Results: A total of 1005 records with oxytocin regulation were included from 124 parturitions involving the VBAC. The XGBoost model performed the best prediction. Through the five fold crossover operation, the accuracy was 0.82, precision was 0.84, the recall rate was 0.80, and the F1 value was 0.82. Among them, the contraction duration, uterine pressure, FHR, UC frequency, and interval time from the previous cesarean section were the variables with great contribution to the modeling.Conclusion: This study constructed an OT regulation model based on the XGBoost, which recognized that the real-time intelligent regulation of the prenatal OT, with fast response speed, high model accuracy, and strong extrapolation had positive significance to obstetric clinical nursing.