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DOI 10.1055/s-0037-1608847

Медицинская библиотека · American journal of perinatology · 2017

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

Kawakita T, Reddy U M, Huang C C, Auguste T C, Bauer D, Overcash R T

СтатьяМногоцентровое исследованиеResearch Support, N.I.H., IntramuralПеревод в очереди

Аннотация

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.

Для цитирования: Kawakita T, Reddy U M, Huang C C, Auguste T C, Bauer D, Overcash R T. Predicting Vaginal Delivery in Nulliparous Women Undergoing Induction of Labor at Term.. American journal of perinatology. 2017. DOI: 10.1055/s-0037-1608847.

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