Fetal diagnosis and therapy · 2018
Clinical and Sonographic Model to Predict Cesarean Delivery after Induction of Labor at Term.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Migliorelli Federico, Baños Núria, Angeles Martina Aida, Rueda Claudia, Salazar Laura, Gratacós Eduard, Palacio Montse
Аннотация
OBJECTIVE: To develop a model combining clinical and sonographic features to predict the risk of cesarean delivery after the induction of labor (IOL).
METHODS: We designed a prospective observational study involving women admitted for IOL. The main outcome was defined as cesarean delivery due to failed IOL or arrest of labor. Several clinical and ultrasonographic variables were collected. Seventy percent of the sample was used to build the predictive model, using stepwise logistic regression, while the remaining sample was used for validation. The final model was estimated and calibrated using all participants.
RESULTS: We analyzed 477 pregnancies. The main outcome occurred in 102/477 (21.4%) women. The final model included previous vaginal delivery (odds ratio [OR] 0.088; 95% confidence interval [CI] 0.04-0.21), height (OR 0.904; 95% CI 0.87-0.94), body mass index before delivery (OR 1.084; 95% CI 1.02-1.15), ultrasonographic estimated fetal weight (OR 3.965; 95% CI 2.18-7.22), and ultrasonographic cervical length (OR 1.065; 95% CI 1.04-1.09) as predictors. Area under the receiver operating characteristics curve was 0.826 (95% CI 0.78-0.87). For a 5% false-positive rate, the sensitivity, specificity, and positive and negative likelihood ratios were 44.1%, 94.9%, 8.7, and 0.59, respectively.
CONCLUSION: Our model combining clinical and ultrasonographic features might offer individualized counseling regarding risk of cesarean delivery to women who are candidates for IOL.
METHODS: We designed a prospective observational study involving women admitted for IOL. The main outcome was defined as cesarean delivery due to failed IOL or arrest of labor. Several clinical and ultrasonographic variables were collected. Seventy percent of the sample was used to build the predictive model, using stepwise logistic regression, while the remaining sample was used for validation. The final model was estimated and calibrated using all participants.
RESULTS: We analyzed 477 pregnancies. The main outcome occurred in 102/477 (21.4%) women. The final model included previous vaginal delivery (odds ratio [OR] 0.088; 95% confidence interval [CI] 0.04-0.21), height (OR 0.904; 95% CI 0.87-0.94), body mass index before delivery (OR 1.084; 95% CI 1.02-1.15), ultrasonographic estimated fetal weight (OR 3.965; 95% CI 2.18-7.22), and ultrasonographic cervical length (OR 1.065; 95% CI 1.04-1.09) as predictors. Area under the receiver operating characteristics curve was 0.826 (95% CI 0.78-0.87). For a 5% false-positive rate, the sensitivity, specificity, and positive and negative likelihood ratios were 44.1%, 94.9%, 8.7, and 0.59, respectively.
CONCLUSION: Our model combining clinical and ultrasonographic features might offer individualized counseling regarding risk of cesarean delivery to women who are candidates for IOL.