Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women

Background: The worldwide rate of cesarean section (CS) is increasing. Development of prediction models for a specific population may improve the unmet need for CS as well as reduce the overuse of CS.Objective: To explore risk factors associated with emergency CS, and to determine the accuracy of predicting it.Methods: A retrospective analysis of the medical records of women who delivered between January 1, 2021-December 2022 was conducted, relevant maternal and neonatal data were retrieved.Results: Out of 1793 deliveries, 447 (25.0%) had emergency CS. Compared to control, the risk of emergency CS was higher in primiparous women (OR 2.13, 95% CI 1.48 to 3.06), in women with higher Body mass index (BMI) (OR 1.77, 95% CI 1.27 to 2.47), in association with history of previous CS (OR 4.81, 95% CI 3.24 to 7.15) and in women with abnormal amniotic fluid (OR 2.30, 95% CI 1.55 to 3.41). Additionally, women with hypertensive disorders had a 176% increased risk of emergency CS (OR 2.76, 95% CI 1.35-5.63). Of note, the risk of emergency CS was more than three times higher in women who delivered a small for gestational age infant (OR 3.29, 95% CI 1.93-5.59). Based on the number of risk factors, a prediction model was developed, about 80% of pregnant women in the emergency CS group scored higher grades compared to control group. The area under the curve was 0.72, indicating a good discriminant ability of the model.Conclusion: This study identified several risk factors associated with emergency CS in pregnant Saudi women. A prediction model showed 72% accuracy in predicting the likelihood of emergency CS. This information can be useful to individualize the risk of emergency CS, and to implement appropriate measures to prevent unnecessary CS.

Automated summary

This study identified several risk factors associated with emergency cesarean section in pregnant Saudi women. A prediction model showed 72% accuracy in predicting the likelihood of emergency cesarean section. This information can be useful to individualize the risk of emergency cesarean section and to implement appropriate measures to prevent unnecessary cesarean sections.