Prediction Model Reliably Forecasts Cesarean Section Conversion in Primiparous Women with Epidural Analgesia

Predicting the need for cesarean section conversion during labor is crucial for optimizing maternal and fetal outcomes, especially in primiparous women receiving epidural labor analgesia. While epidural analgesia is highly effective for pain management, the decision to convert to a cesarean section can be complex and often made intrapartum, necessitating a tool for earlier risk stratification. Current clinical assessments can be subjective, leading to delays or suboptimal resource allocation. This study addresses the gap by developing a robust prediction model to identify high-risk individuals proactively.

This retrospective cohort study included 1020 primiparous women who received epidural labor analgesia at a tertiary hospital in Wuxi, China. The cohort was split into a training group (March-December 2023) and a validation group (January-March 2024). Researchers extracted maternal, fetal, and intrapartum variables from electronic medical records, anesthesia records, and fetal monitoring systems. A multivariable logistic regression model was developed to predict conversion to cesarean section, and a nomogram was constructed. Model performance was assessed using area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and calibration plots, with decision curve analysis evaluating clinical utility.

The rate of conversion to cesarean section was 15.9% in the training cohort and 15.3% in the validation cohort, indicating a consistent incidence. Six independent predictors were identified: maternal age, intrapartum temperature, amniotic fluid characteristics, fetal heart rate status, use of oxytocin, and rupture of membranes during the first stage of labor. The prediction model demonstrated good discrimination in the training cohort, achieving an AUC of 0.810 (sensitivity 0.822, specificity 0.606).