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 (AUC) 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<0.001).

Conclusion This dual-output, user-friendly, and admission-based web system enhances interpretability and supports personalized counseling and evidence-based decision-making. Specifically designed for Korean women, it enables the early identification of high-risk cases and may help reduce unnecessary operative interventions. Therefore, further multicenter studies are warranted.