Construction and verification of prediction models for intrapartum cesarean section to be used at full-term pregnant women with singleton and cephalic presentation under the new labor standard: a retrospective case-control study

Abstract

Abstract Purpose: This study aimed to explore the risk factors associated with intrapartum cesarean section and construct prediction models for intrapartum cesarean section under the new labor standard. Methods: This was a retrospective case-control study at Peking University Third Hospital in China from June 2020 to October 2021. Full-term pregnant women were allowed to have a trial of labor, including singleton and cephalic presentation. Herein, a total of 20 parameters relevant to maternal and obstetric characteristics were listed as candidate predictors. In addition, univariate and multivariate logistic regression analyses were used to construct the prediction model. Two prediction models were included: Model A is for the gravida before the trial of labor and model B is for those already at the active phase. Then, the calibration, discrimination, and clinical utility of the models was assessed and an internal validation was performed. Finally, the models were ultimately transformed into nomograms for clinical use. Results: The predictors in model A were older maternal age, shorter in height, longer gestational age, heavier in weight, primipara, lower Bishop score, complicated hypertensive disorder, receiving labor induction and heavier estimation of fetal weight(EFW) within one week before delivery. In model B, height, weight gain during pregnancy, complicated hypertensive disorder, receiving labor induction, analgesia, assist reproductive technology, latent period duration,delivery at night and EFW were included. The models showed satisfactory discrimination, calibration, and high clinical net benefit. Conclusion: The prediction models for an intrapartum cesarean section can accurately predict the risk of cesarean section; This prediction model can provide guidance for clinicians in the management of labor, grasp the appropriate indications for cesarean section, avoid excessive trial labor, and reduce maternal and infant complications.