Factors Predicting Successful Vaginal Delivery Following Induction of Labor in Term Pregnancy

Abstract

Purpose: This study was proposed to evaluate factors predicting a successful vaginal delivery following labor induction and develop induction prediction model in term pregnancy among Thai pregnant women. Patients and Methods: We conducted a retrospective cohort study using electronic medical records of 23,833 deliveries from April 2010 to July 2021 at tertiary care university hospital in Bangkok, Thailand. Univariate regression was performed to identify the association of individual parameters to successful vaginal delivery. Multiple logistic regression analysis of all possible variables from univariate analysis was performed to develop a prediction model with statistically significant of p value <0.05. Results: Of the total 809 labor-induced pregnancies, the vaginal delivery rate was 56.6%. Among predicting variables, history of previous vaginal delivery (aOR 5.75, 95% CI 3.701–8.961), maternal delivery BMI <25 kg/m2 (aOR 2.010, 95% CI 1.303–3.286), estimated fetal weight <3500 g (aOR 2.193, 95% CI 1.246–3.860), and gestational age ≤39 weeks (aOR 1.501, 95% CI 1.038–2.173) significantly increased the probability of a successful vaginal delivery following labor induction. The final prediction model has been internally validated. Model calibration and discrimination were satisfactory with Hosmer–Lemeshow test P = 0.21 and with AUC of 0.756 (95% CI 0.695–0.816). Conclusion: This study determined the pragmatic predictors for successful vaginal delivery following labor induction comprised history of previous vaginal delivery, maternal delivery BMI <25 kg/m2, estimated fetal weight <3500 g, and gestational age ≤39 weeks. The final induction prediction model was well-performing internally validated prediction model to estimate individual probability when undergoing induction of labor. Despite restricted population, the predicting factors and model could be useful for further prospective study and clinical practice to improve induction outcomes.