Predicting cesarean in the second stage of labor

Area of Science:

• Obstetrics and Gynecology
• Maternal-Fetal Medicine
• Clinical Prediction Modeling

Background:

• Cesarean delivery (CD) is a common surgical procedure.
• The second stage of labor presents unique challenges for predicting delivery mode.
• Accurate prediction of CD is crucial for optimizing maternal and neonatal outcomes.

Purpose of the Study:

• To develop and evaluate prediction models for cesarean delivery (CD) in the second stage of labor.
• To identify key factors influencing the decision for CD at 10-cm dilation.
• To compare the predictive performance of logistic regression and classification and regression tree (CART) analysis.

Main Methods:

• Retrospective cohort study of term women reaching 10-cm dilation.
• Analysis included logistic regression and CART analysis.
• Predictors were limited to factors known at 10-cm dilation.

Main Results:

• 1.6% of 5,388 women required CD at 10-cm dilation.
• Logistic regression identified 4 risk factors with an AUC of 0.75.
• CART analysis identified fetal station as the primary predictor but had low classification accuracy (19.3% for CD).

Conclusions:

• Neither logistic regression nor CART models can reliably predict second-stage cesarean delivery.
• Antenatal and intrapartum characteristics available at 10-cm dilation are insufficient for accurate prediction.
• Further research is needed to identify more effective prediction strategies.