Development and validation of models to predict cesarean delivery among low-risk nulliparous women at term: a retrospective study in China

Abstract Background A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict intrapartum cesarean delivery for low-risk nulliparous women in Chinese population.Methods We conducted a retrospective cohort study of low-risk nulliparous women with singleton, term, cephalic pregnancies. A predictive model for cesarean delivery was derived using univariate and multivariable logistic regression from the hospital of the First Affiliated Hospital of Soochow University. External validation of the prediction model was then performed using the data from Sihong county People’s Hospital. A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance.Results The intrapartum cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6,551) and 7.82% (599/7,657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. We had established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The two prediction models were well-calibrated with Hosmer-Lemeshow test P=0.263 and P=0.817, respectively. Decision curve analysis demonstrated that two models had clinical application value, and they provided greatest net benefit between threshold probabilities of 4% to 60%. And internal validation using Bootstrap method demonstrated similar discriminatory ability. We external validated the model involving fetal sex, for which the AUC was 0.775, while the slope and intercept of the calibration plot were 0.979 and 0.004, respectively. On the external validation set, another model had an AUC of 0.775 and a calibration slope of 1.007. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.