The journal of obstetrics and gynaecology research · 2019
Building a predictive model for successful vaginal delivery in nulliparas with term cephalic singleton pregnancies using decision tree analysis.
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Zhao Rui-Fen, Zhang Wei-Yuan, Zhou Li, Chen Yi
Аннотация
AIM: To establish a model predicting successful vaginal delivery (VD) in nulliparas with term cephalic singleton pregnancies.
METHODS: We retrospectively identified 6799 term nulliparas with cephalic singletons (6416 VD and 383 cesarean section [CS] due to dystocia) who entered labor (cervical dilation ≥2 cm) between September 2014 and August 2015. Using VD as the dependent variable and age, maternal body height, educational attainment, gravidity, gestational age, pre-pregnancy body mass index (BMI), BMI upon admission for delivery, gestational weight gain, gestational hypertension and gestational diabetes as the independent variables, predictors of VD success were identified using a multivariate binary logistic regression and then ranked with decision-tree analysis.
RESULTS: While multiple factors are associated with improved VD success, we found body height, gestational age, and intrapartum BMI to be the best predictors of successful VD. Our predictive model has a classification accuracy, sensitivity and specificity of 76.6%, 96.7% and 16.4%, respectively, and it was subsequently confirmed by both internal and external validation.
CONCLUSION: Our predictive model indicates body height, gestational age and intrapartum BMI as the major predictors of successful VD in low-risk patients.
METHODS: We retrospectively identified 6799 term nulliparas with cephalic singletons (6416 VD and 383 cesarean section [CS] due to dystocia) who entered labor (cervical dilation ≥2 cm) between September 2014 and August 2015. Using VD as the dependent variable and age, maternal body height, educational attainment, gravidity, gestational age, pre-pregnancy body mass index (BMI), BMI upon admission for delivery, gestational weight gain, gestational hypertension and gestational diabetes as the independent variables, predictors of VD success were identified using a multivariate binary logistic regression and then ranked with decision-tree analysis.
RESULTS: While multiple factors are associated with improved VD success, we found body height, gestational age, and intrapartum BMI to be the best predictors of successful VD. Our predictive model has a classification accuracy, sensitivity and specificity of 76.6%, 96.7% and 16.4%, respectively, and it was subsequently confirmed by both internal and external validation.
CONCLUSION: Our predictive model indicates body height, gestational age and intrapartum BMI as the major predictors of successful VD in low-risk patients.