Dual-output, web-based risk assessment system for cesarean section due to dystocia: integration of logistic regression and risk scoring models

OBJECTIVE: To develop a web-based risk assessment system to predict cesarean section (CS) due to dystocia at admission in nulliparous term singleton vertex pregnancies, tailored for Korean women. METHODS: This case-control study analyzed the data of 126 women with CS due to dystocia and 490 women who had vaginal deliveries. Eight predictors-gestational age, maternal age, maternal height, pre-gestational body mass index, birth weight, fetal sex, cervical dilatation at admission, and maternal-fetal ratio-were identified using multivariate logistic regression. The system integrated both logistic regression and risk-scoring models simultaneously to provide individualized risk probabilities and categorical risk levels. RESULTS: The model demonstrated strong predictive accuracy, with an area under the receiver operating characteristic curve of 0.86. Risk stratification classified the patients into low-, intermediate-, and high-risk groups, corresponding to CS rates of 1.6, 47.6, and 50.8%, respectively (p