Factors predicting successful labor induction with dinoprostone and misoprostol vaginal inserts

To evaluate the maternal and pregnancy characteristics that independently predict successful induction of labor, defined as vaginal delivery.

The study was a secondary analysis of the data collected during the Misoprostol Vaginal Insert Trial, a multisite, double-blind, randomized trial of women requiring cervical ripening before induction of labor. The primary outcome was to estimate the maternal and pregnancy characteristics that independently predict successful induction of labor. Univariable and multiple regression analyses were performed for maternal and pregnancy-related factors that potentially could predict a successful induction of labor.

A total of 1,274 patients had sufficient labor and delivery data for a comparative analysis. Nine hundred sixteen (72%) induced patients subsequently had vaginal deliveries. Multiparity (odds ratio [OR] 4.63, 95% confidence interval [CI] 3.39-6.32, P<.001), maternal body mass index (BMI) less than 30 (OR 1.69, 95% CI 1.32-2.22, P<.001) and height greater than 5'5" (OR 1.47, 95% CI 1.15-1.9, P=.002), baseline modified Bishop score of 4 (OR 2.15, 95% CI 1.12-4.20, P=.047), and birth weight below 4,000 g (OR 2.17, 95% CI 1.51-3.13, P<.001) were significant for predicting successful induction of labor. Logistic regression analysis was performed to evaluate each factor as an independent predictor. In addition to the above-mentioned factors, maternal age younger than 35 years (OR 1.81, 95% CI 1.15-2.86, P=.01) and Hispanic race (OR 1.45, 95% CI 1.02-2.05, P=.036) each proved to significantly favor a successful induction. Conversely, African-American race was correlated with a higher incidence of cesarean delivery (OR 1.47, 95% CI 1.02-2.13, P<.001).

Maternal characteristics such as BMI, parity, age, and race and neonatal birth weight are important variables to consider when predicting a successful induction of labor. The nearly 30% rate of cesarean delivery in this study underscores the importance of selecting appropriate candidates.

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