Predicting Vaginal Delivery in Nulliparous Women Undergoing Induction of Labor at Term

Author:

Reddy U. M.1,Huang C. C.23,Auguste T. C.14,Bauer D.4,Overcash R. T.1,Kawakita T.1

Affiliation:

1. Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, MedStar Washington Hospital Center, Washington, District of Columbia

2. Department of Biostatistics and Bioinformatics, MedStar Health Research Institute, Hyattsville, Maryland

3. Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia

4. MedStar Simulation Training & Education Lab, Washington, District of Columbia

Abstract

Objective We sought to develop a model to calculate the likelihood of vaginal delivery in nulliparous women undergoing induction at term. Study Design We obtained data from the Consortium on Safe Labor by including nulliparous women with term singleton pregnancies undergoing induction of labor at term. Women with contraindications for vaginal delivery were excluded. A stepwise logistic regression analysis was used to identify the predictors associated with vaginal delivery by considering maternal characteristics and comorbidities and fetal conditions. The receiver operating characteristic curve, with an area under the curve (AUC) was used to assess the accuracy of the model. Results Of 10,591 nulliparous women who underwent induction of labor, 8,202 (77.4%) women had vaginal delivery. Our model identified maternal age, gestational age at delivery, race, maternal height, prepregnancy weight, gestational weight gain, cervical exam on admission (dilation, effacement, and station), chronic hypertension, gestational diabetes, pregestational diabetes, and abruption as significant predictors for successful vaginal delivery. The overall predictive ability of the final model, as measured by the AUC was 0.759 (95% confidence interval, 0.749–0.770). Conclusion We identified independent risk factors that can be used to predict vaginal delivery among nulliparas undergoing induction at term. Our predictor provides women with additional information when considering induction.

Publisher

Georg Thieme Verlag KG

Subject

Obstetrics and Gynaecology,Pediatrics, Perinatology, and Child Health

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