Development and clinical application of an intrapartum ultrasound-based predictive model for cesarean section conversion in nulliparous women following unsuccessful vaginal delivery attempts: A single-center retrospective study

Ultrasound is potentially more accurate for identifying women who are likely to have a spontaneous vaginal birth than conventional labor progression assessments. However, research on the application of intrapartum ultrasound measurements in predicting cesarean section (CS) during vaginal trials of labor in full-term low-risk nulliparous women is lacking. We aimed to develop a predictive model using intrapartum ultrasound data for CS conversion in nulliparous women who experienced a failed vaginal delivery trial and integrate this model into clinical practice. Low-risk nulliparous women carrying full-term singleton vertex fetuses were randomly divided into derivation and validation cohorts (ratio 8:2). Ultrasound was used to measure the angle of progression and head-perineum distance of a cervical dilation of 4 to 6 cm. Maternal age, gestational age, height, weight, and other factors were included. Univariate and binary logistic regression analyses were conducted to identify factors affecting failed vaginal delivery trials and subsequent CS conversion. A risk prediction model was constructed, and its discrimination, calibration, and clinical usefulness were assessed. Independent predictors of CS conversion included oxytocin use for induction, fetal head circumference, estimated fetal weight, labor analgesia, and angle of progression when the active labor phase commenced (P < .05). The nomogram showed good discrimination and calibration. The receiver operating characteristic curve area for the derivation and validation cohorts were 0.924 (95% confidence interval: 0.892-0.956) and 0.916 (95% confidence interval: 0.817-01.000), respectively, with sensitivities and specificities of 0.933 and 0.781 for the derivation cohort and 0.857 and 0.827 for the validation cohort, respectively. Concordance indexes for the derivation and validation cohorts were 0.92 and 0.91, respectively. The predictive model exhibited robust predictive capabilities and high precision. It can provide a reference for clinicians to guide women to choose the appropriate mode of delivery, thereby improving maternal and infant outcomes.