An Intrapartum Ultrasound-Based Predictive Model for Cesarean Section Conversion in Nulliparous Women Following Unsuccessful Vaginal Delivery Attempts

An Intrapartum Ultrasound-Based Predictive Model for Cesarean Section Conversion in Nulliparous Women Following Unsuccessful Vaginal Delivery Attempts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An Intrapartum Ultrasound-Based Predictive Model for Cesarean Section Conversion in Nulliparous Women Following Unsuccessful Vaginal Delivery Attempts Zhanpeng Yu, Shunlan Du, Lili Xu, Yun Cheng, Feijun Hu, Qiaoyang Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6992403/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to develop a predictive model for cesarean section conversion in nulliparous women using intrapartum ultrasound data. Low-risk nulliparous women carrying full-term singleton vertex fetuses were divided into derivation and validation cohorts. Ultrasound was used to measure the angle of progression and head-perineum distance for a cervical dilation of 4–6 cm. Regression analyses identified factors affecting failed vaginal delivery trials and subsequent cesarean section conversion, and a risk prediction model was constructed. Independent predictors of cesarean section conversion included oxytocin use for induction, fetal head circumference, estimated fetal weight, labor analgesia, and angle of progression when the active labor phase commenced. 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. 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 assist clinicians in the choice of the appropriate mode of delivery, thereby improving maternal and infant outcomes. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research predictive model intrapartum ultrasound angle of progression cesarean section conversion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Vaginal birth, which closely aligns with the natural course of human development, is widely recognized as the optimal mode of delivery, providing substantial benefits for maternal and infant health, facilitating postpartum recuperation, and promoting healthier outcomes in future pregnancies. Most expectant mothers prefer vaginal delivery. However, its unpredictability is influenced by several factors, including maternal and fetal conditions, strength of uterine contractions, dimensions of the birth canal, and emotional states. Among nulliparous women, the rate of cesarean section following an unsuccessful vaginal delivery was approximately 13.54% [ 1 ]. An unsuccessful trial increases the risk of serious complications, including postpartum hemorrhage, maternal infection, neonatal metabolic acidosis, and intracranial hemorrhage, posing long-term health risks to both mothers and children [ 2 ]. If labor abnormalities arise during the first stage, delaying a cesarean section can complicate the procedure and increase the likelihood of surgical and postsurgical complications such as bleeding, uterine incision damage, amniotic fluid infection, and neonatal injury. Therefore, timely detection of abnormalities in the first stage of labor, prompt intervention, choosing the appropriate time for cesarean section, and improving the quality of obstetric clinical practice have become urgent issues in the field of obstetrics. Conventional assessment of labor progression mainly depends on multiple vaginal examinations by medical staff to monitor cervical dilation and fetal descent, which may cause discomfort, infection, and genital tract trauma [ 3 ]. Vaginal examination of cervical dilation and descent of the fetal presenting part is highly subjective and may not accurately identify the fetal position in some cases. The 2019 International Society of Ultrasound in Obstetrics and Gynecology guidelines for intrapartum ultrasound practice suggest that using ultrasound during labor management is increasingly recognized for its enhanced precision and reliability over traditional clinical assessments [ 4 ]. Research indicates that ultrasound can more accurately determine fetal head position and station, predict labor arrest, and potentially identify women who are likely to have a spontaneous vaginal birth compared with those who may require surgical intervention. Moreover, emerging evidence suggests that ultrasonography during labor can predict the success of instrumental vaginal delivery, providing a valuable tool for improving the prediction and management of childbirth outcomes. Many scholars have proposed models [ 5 – 7 ] based on the clinical parameters of pregnant women and fetuses, as well as prenatal ultrasound variables, to predict the risk of cesarean section in pregnant women with singleton full-term cephalic presentation. However, research on the application of intrapartum ultrasound measurements in predicting cesarean section during vaginal trials of labor in full-term low-risk nulliparous women is lacking. Therefore, this study aimed to construct a nomogram for predicting cesarean section during the onset of the active phase of the first stage of labor based on perineal ultrasound parameters (angle of progression [AOP] and head-perineum distance [HPD]) combined with factors such as maternal age, gestational age, body mass index (BMI), and the use of labor analgesia. Methods Study participants This forward-looking cohort study enrolled 432 full-term, low-risk nulliparous women admitted for vaginal delivery trials at the Obstetrics Department of Dongyang Hospital, affiliated with Wenzhou Medical University, between June 2021 and January 2024. Participants were selected according to strict predefined inclusion and exclusion criteria to ensure a robust and representative sample. The inclusion criteria were as follows: primiparous women in labor, full-term singleton fetuses in a cephalic presentation, fetal malformations excluded by prenatal check-ups, no pelvic structural abnormalities in pregnant women, no pregnancy complications or comorbidities, and no contraindications for vaginal delivery. The exclusion criteria were as follows: macrosomic infants (> 4000 g) or low-birthweight infants (< 2500 g), those who refused vaginal delivery trials or ultrasound examinations, and those who requested cesarean section without surgical indications. All examinations and procedures were conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the Dongyang People’s Hospital (DRY-2022-YX-066). The study was conducted strictly following the stipulated procedures, and all pregnant women provided written informed consent. Patient information The following information was collected from patient medical records: mode of delivery, including spontaneous delivery, assisted vaginal delivery (including vacuum extraction, forceps delivery, and perineal laceration repair), and cesarean section; maternal age, height, BMI, gestational age, fundal height, abdominal circumference, and estimated fetal weight (fetal weight = fundal height × abdominal circumference ± 200 g); prenatal ultrasound examination, including fetal head circumference, abdominal circumference, and estimated fetal weight (based on the last B-mode ultrasound result before delivery); induction methods (oxytocin, cervical balloon, and dinoprostone pessary induction; administration of labor analgesia; surgical indications for cesarean section conversion after failed vaginal delivery; and neonatal weight and Apgar score. Gestational age was verified by crown-rump length during early pregnancy. Induction methods Methods of induction were classified as no induction, oxytocin induction (induction using intravenous drip of oxytocin alone), cervical balloon induction (with or without oxytocin or artificial membrane rupture), and dinoprostone pessary induction (with or without oxytocin or artificial membrane rupture). Cesarean section indications The indications for cesarean section were fetal distress in utero, arrest of the active phase, and prolonged second stage of labor. The definitions are contained in the relevant guidelines and consensus documents [ 8 , 9 ]. Neonatal asphyxia was defined by an Apgar score of ≤ 7 at 1 or 5 min after birth. Ultrasound imaging A Mindray Model 7 ultrasound diagnostic device was used in the second- and third-trimester modes with a convex array transducer, and the transducer frequency was 3.0–5.0 MHz. During the first stage of labor, when cervical dilation reached 4–6 cm [ 9 ], the examination was initiated, and ultrasound measurements were taken between contractions. The pregnant woman was placed in the lithotomy position, the bladder was emptied, and the HPD and AOP were measured (Fig. 1 ). Two trained obstetricians performed the ultrasound examination using the same model as the portable ultrasound machine, employing the “blind method” to measure and record the ultrasound data of the pregnant women without sharing the results. Each ultrasound measurement was repeated twice, and the average value was considered the final result. A midwife with more than 3 years of work experience performed the vaginal examination, with the examiner present throughout, and results were not shared between the ultrasound and vaginal examiners. Data analysis and model development Using the rms package in R version 4.1.1, we categorized parturients into two distinct cohorts based on their delivery method: those who delivered vaginally and those who underwent cesarean section. We analyzed the collected parturient data to delineate epidemiological profiles. Subsequently, a group-wise analysis was performed. To ensure the reliability of model validation, we used a random stratified sampling method to divide the entire dataset into a training set (n = 346) and a validation set (n = 86) in an 8:2 ratio. In the training set, a univariate analysis was initially conducted, followed by multivariate analysis using binary logistic regression. This approach was used to identify and quantify factors influencing the likelihood of cesarean section conversion. The regression model encompassed various statistical variables potentially affecting the delivery mode, with cesarean section conversion as the dependent variable and the previously listed factors as independent variables. We calculated the model’s discrimination (C-statistic) and calibration (Hosmer–Lemeshow test). External validation was performed in the validation set to assess the model’s generalizability. We constructed a nomogram, a graphical representation designed for ease of clinical use, which assigns scores to each variable based on individual test results, with the aggregated score reflecting the estimated risk of cesarean section conversion. The validity of the model was ascertained through separate evaluations using both derivation and validation cohorts. We assessed the model’s discriminatory power (concordance index), its calibration (via calibration plots), and its diagnostic accuracy (via the receiver operating characteristics [ROC] curve) for each cohort. Subsequently, an overall evaluation of the efficacy of the model was conducted. Furthermore, we performed a decision curve analysis (DCA) to enhance our understanding of the model’s clinical utility. Results Study participants Out of the 432 low-risk nulliparous women with full-term singleton vertex fetuses who underwent vaginal delivery, 380 women successfully delivered vaginally (including eight forceps-assisted cases), and 52 women underwent a cesarean section, resulting in a cesarean delivery rate of 12.03%. Table 1 compares the demographic and clinical profiles of the two participant cohorts. Figure 2 provides a visual representation of the study methodology as a flowchart illustrating the process from recruitment to data analysis. Table 1 Patient characteristics Variables Level Overall 0 (Vaginal delivery) 1 (Cesarean delivery) P n 432 380 52 Age, y (median [IQR]) 27.50 [25.00, 31.00] 27.00 [25.00, 31.00] 28.00 [25.75, 31.00] 0.98 Height, cm (median [IQR]) 160.00 [155.00, 163.00] 160.00 [155.00, 163.00] 158.00 [155.00, 161.00] 0.23 Weight, kg (median [IQR]) 68.75 [62.00, 76.00] 68.30 [62.00, 76.00] 69.50 [64.75, 77.00] 0.37 Ges, weeks (median [IQR]) 39.00 [38.00, 40.00] 39.00 [38.00, 40.00] 39.00 [39.00, 40.00] 0.072 BMI, kg/m 2 (median [IQR]) 27.23 [25.00, 29.36] 27.07 [24.75, 29.32] 27.80 [25.76, 29.65] 0.085 NI, n (%) 0 185 (42.8) 133 (35.0) 52 (100.0) < 0.001 1 247 (57.2) 247 (65.0) 0 (0.0) OI, n (%) 0 273 (63.2) 247 (65.0) 26 (50.0) 0.035 1 159 (36.8) 133 (35.0) 26 (50.0) CbI, n (%) 0 411 (95.1) 380 (100.0) 31 (59.6) < 0.001 1 21 (4.9) 0 (0.0) 21 (40.4) DpI, n (%) 0 427 (98.8) 380 (100.0) 47 (90.4) < 0.001 1 5 (1.2) 0 (0.0) 5 (9.6) Labor analgesia, n (%) 0 240 (55.6) 220 (57.9) 20 (38.5) 0.008 1 192 (44.4) 160 (42.1) 32 (61.5) Head, cm (median [IQR]) 331.00 [324.00, 339.00] 331.00 [324.00, 338.00] 336.00 [329.75, 342.00] < 0.001 Ab, cm(median [IQR]) 341.00 [331.00, 350.00] 340.00 [331.00, 349.00] 348.00 [338.75, 359.00] < 0.001 FHw, g (median [IQR]) 3303.50 [3031.50, 3498.50] 3283.00 [3013.00, 3484.00] 3469.50 [3213.75, 3661.25] < 0.001 Uw, g (median [IQR]) 3366.00 [3165.00, 3640.00] 3333.00 [3128.00, 3600.00] 3613.00 [3392.00, 3821.75] < 0.001 AOP, ° (median [IQR]) 136.20 [130.50, 144.10] 138.15 [131.75, 144.62] 128.45 [123.18, 129.65] < 0.001 HPD, cm (median [IQR]) 1.89 [1.51, 2.08] 1.86 [1.45, 2.06] 2.16 [1.99, 2.31] < 0.001 IQR: interquartile range; Ges: gestational weeks; BMI: body mass index; NI: no induction; OI: oxytocin induction; CbI: cervical balloon induction; DpI: dinoprostone pessary induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference; Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance Fetal distress, accounting for 46.2% of cesarean section conversions (24 of 52 cases), was the primary reason for switching from vaginal to cesarean delivery, followed by arrest of the active phase of labor (28.8%, 15 of 52 cases). A detailed breakdown of these indications is presented in Table 2 . Table 2 Indications for cesarean section after failed vaginal delivery (n = 52) Indications for cesarean section n Proportion (%) Fetal distress 24 46.2 Active phase arrest 15 28.8 Arrest of fetal head descent 6 11.5 Prolonged second stage of labor 3 5.8 Placental abruption 3 5.8 Chorioamnionitis 1 1.9 Establishing the predictive model In the univariate analysis, the 432 participants were categorized into two cohorts based on their vaginal delivery attempts: vaginal delivery and cesarean section. The analysis compared data pertinent to each group and found no statistically significant disparities in maternal age, gestational age, height, weight, predelivery BMI, and gestational age at the time of delivery among the nulliparous women in either cohort, irrespective of the induction method employed (none, cervical balloon, or dinoprostone pessary), with P-values exceeding the threshold of 0.05. In contrast, significant differences were observed (P < 0.05) when evaluating predelivery ultrasound measurements, such as fetal head and abdominal circumferences and ultrasound-estimated fetal weight. Discrepancies were also observed in the estimated fetal weight derived from fundal height and abdominal circumference measurements, oxytocin use for induction, labor analgesia, AOP, and HPD values recorded at the onset of the active labor phase. A detailed examination of these findings is presented in Table 3 . Table 3 Results of univariate logistic regression Variables Partial regression coefficient Standard error Z-value OR (95% CI) P-value Age -0.020 0.034 0.591 0.980 (0.917, 1.048) 0.554 Height -0.030 0.028 1.067 0.970 (0.918, 1.026) 0.286 Weight 0.011 0.016 0.698 1.012 (0.979, 1.045) 0.485 Ges 0.212 0.153 1.380 1.236 (0.915, 1.669) 0.168 BMI 0.058 0.044 1.314 1.059 (0.972, 1.154) 0.189 NI 0 Reference 1 -19.728 1256.873 0.016 0.000 (0.000, Inf) 0.987 OI 0 Reference 1 0.782 0.322 2.427 2.187 (1.163, 4.113) 0.015 CbI 0 Reference 1 19.948 959.515 0.021 460395647.826 (0.000, Inf) 0.983 DpI 0 Reference 1 17.566 727.699 0.024 42551641.385 (0.000, Inf) 0.981 Labor analgesia 0 Reference 1 0.678 0.326 2.082 1.969 (1.040, 3.728) 0.037 Head 0.056 0.017 3.365 1.058 (1.024, 1.093) 0.001 Ab 0.035 0.011 3.050 1.035 (1.012, 1.058) 0.002 FHw 0.002 0.001 3.307 1.002 (1.001, 1.003) 0.001 Uw 0.002 0.000 3.827 1.002 (1.001, 1.002) 0.000 AOP -0.243 0.036 6.666 0.784 (0.730, 0.842) 0.000 HPD 4.729 0.838 5.642 113.202 (21.896, 585.256) 0.000 OR: odds ratio; CI: confidence interval; Ges: gestational weeks; BMI: body mass index; NI: no induction; OI: oxytocin induction; CbI: cervical balloon induction; DpI: dinoprostone pessary induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance Using the data of 432 nulliparous women, we performed a comprehensive multivariate analysis, incorporating variables that demonstrated statistical significance in the preceding univariate analysis. The binary outcome variable distinguished between successful vaginal delivery (coded as 0) and failed attempt resulting in cesarean section conversion (coded as 1). The findings revealed several independent predictors of cesarean section conversion, including the administration of oxytocin for labor induction, predelivery ultrasound measurements of fetal head circumference, ultrasound-estimated fetal weight, provision of labor analgesia, and AOP at the commencement of the active phase of labor, all with a P-value of less than 0.05. The detailed results are presented in Table 4 . Table 4 Results of multivariate logistic regression Variables Partial regression coefficient Standard error Z-value OR (95% CI) P-value OI 0 Reference 1 1.237 0.474 2.609 3.446 (1.360, 8.728) 0.009 Labor analgesia 0 Reference 1 1.504 0.493 3.048 4.500 (1.711, 11.836) 0.002 Head 0.076 0.037 2.081 1.079 (1.004, 1.159) 0.037 Ab 0.013 0.041 0.322 1.013 (0.936, 1.097) 0.747 FHw -0.001 0.002 0.383 0.999 (0.995, 1.003) 0.702 Uw 0.002 0.001 3.087 1.002 (1.001,1.004) 0.002 AOP -0.274 0.057 4.814 0.760 (0.680, 0.850) 0.000 HPD 0.867 1.204 0.720 2.379 (0.225, 25.174) 0.471 OR: odds ratio; CI: confidence interval; OI: oxytocin induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference; Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance We used the rms package in R software to develop a nomogram model that integrated the risk factors for cesarean section conversion identified through our multivariate logistic regression analysis (Fig. 3 ). This nomogram assigns a specific score to each parameter for an individual nulliparous case based on variables associated with the risk of cesarean section conversion. By summing these individual scores, we obtained a total score for each woman, which we used to estimate the likelihood of cesarean section conversion via the total score axis on the nomogram. The cesarean section conversion prediction model was evaluated as follows. The calculated values for the predictive model were utilized as the predictor variable, while the binary outcome of the cesarean section conversion served as the criterion variable. Through ROC curve analysis, we determined the model’s discriminative ability. For the group used to construct the model, the area under the ROC curve (AUC) was 0.924 (95% confidence interval [CI]: 0.892–0.956). The maximum Youden index of the model was 0.714, corresponding to an optimal cutoff value of 0.099. This yielded a sensitivity of 0.933 and a specificity of 0.781. For the validation group, the AUC of the ROC curve was 0.916 (95% CI: 0.817–1.000), and sensitivity and specificity were 0.857 and 0.827, respectively. A visual representation of these results is shown in Fig. 4 . The concordance indexes, which quantify the model’s overall correctness, were 0.92 for the derivation cohort and 0.91 for the validation cohort. The predicted probability of cesarean section during the labor trial showed good consistency with the actual observed probability (Figs. 5 and 6 ). DCA is a clear and accessible method for evaluating the practical clinical value and potential impact of predictive modeling tools. Our application of DCA to the nomogram developed in this study demonstrated its enhanced capacity to accurately forecast the likelihood of cesarean section conversion, offering valuable insights for clinical decision-making. Figure 7 shows a visual representation of the DCA findings. Discussion Traditionally, labor progression is monitored through repeated vaginal examinations by obstetricians and midwives. Being non-invasive, ultrasound examination offers substantial benefits in terms of safety and repeatability. This approach is widely accepted by pregnant women because of its minimal discomfort and capacity to prevent infections that can arise from multiple invasive procedures. Compared to the traditional conventional method of vaginal palpation, perineal ultrasound and other ultrasound-guided techniques provide greater precision in monitoring fetal head descent and cervical dilation. Quantifiable and consistent ultrasound methods allow a more objective evaluation, considerably reducing subjective interpretation by medical personnel, thereby enhancing the precision and impartiality of assessing labor progression [ 10 ]. In this study, we prioritized perineal ultrasound parameters, specifically AOP and HPD, measured at the onset of the active phase in the first stage of labor, as pivotal determinants. These were considered alongside maternal demographics, including age, gestational age, BMI, and use of labor analgesia. Using logistic regression analysis, we developed a forward-looking predictive model tailored for low-risk full-term pregnancies in nulliparous women based on initial AOP and HPD values. Additionally, we developed a nomogram, a user-friendly graphical tool, to streamline personalized and methodical assessment of labor progress and forecast the risk of cesarean section conversion. This instrument is particularly beneficial for women who are at an elevated risk of requiring a cesarean section because it aids in the prompt detection of labor irregularities during the first stage. Early recognition of such abnormalities is crucial for avoiding delayed cesarean section, which is instrumental in averting negative outcomes for both mothers and children. Our research identified key determinants that significantly influenced the likelihood of cesarean section conversion, including the use of oxytocin for labor induction, the initial AOP during the active phase of the first stage of labor, fetal head circumference as ascertained through predelivery ultrasound, fetal weight estimation derived from ultrasound, and administration of labor analgesia. These findings are consistent with the prevailing outcomes observed in related studies. The use of oxytocin for induction may affect the risk of conversion [ 11 ]. Considering the accuracy and advantages of intrapartum ultrasound in assessing abnormal labor progress, our study specifically included the AOP value at the beginning of the active phase of the first stage of labor as one of the factors and concluded that the AOP value is a protective factor for cesarean section conversion. The larger the AOP value measured via ultrasound at the beginning of the active phase of the first stage of labor, the lower the probability of cesarean section conversion, which is similar to the results of previous research [ 12 – 14 ]. This study determined the AOP cutoff value to be 125.6° using the ROC curve; a value less than 125.6° indicates a higher probability of cesarean section conversion. Eggebø et al. [ 13 ] reported that when the AOP was greater than 110°, the cesarean section rate was 12%, whereas when the AOP was less than 100°, the cesarean section rate increased to 62%. Some studies suggest that the risk of emergency cesarean section increases when ultrasound-estimated fetal weight is high [ 5 , 15 ]. Amini et al. [ 7 ] showed that for every 1 cm increase in fetal head circumference, the odds ratio for the risk of cesarean section is 1.176 (95% CI: 1.112–1.243). Ejigu et al. [ 16 ] reported that the odds of failed induction were 4.3 times higher among women who were administered analgesia/anesthesia (adjusted odds ratio = 4.37 [1.31–14.59]) than among those who were not. These results are similar to those of this study. Levine et al. [ 17 ] proposed in their study that maternal height is a protective factor for cesarean section conversion during vaginal delivery trials; the higher the height, the lower the probability of cesarean section conversion. Other researchers have suggested that indicators such as age, gestational age, and pre-pregnancy BMI are risk factors for cesarean section conversion [ 18 – 21 ]; the older the age, the higher the pre-pregnancy BMI, and the greater the gestational age, the higher the probability of a cesarean section. Additionally, various factors such as ethnicity and social status can affect the final outcomes of childbirth [ 22 , 23 ]. Scholars have already constructed predictive models for cesarean section conversion following failed vaginal delivery trials in full-term singleton vertex presentations among nulliparous women by combining multiple indicators. Tolcher et al. [ 24 ] conducted a study involving 785 first-time mothers with single, full-term (at least 37 weeks), head-first pregnancies who were undergoing labor induction and identified several independent risk factors linked to a higher likelihood of cesarean delivery. These factors include maternal age, stature, BMI, weight gain during gestation, gestational age at the time of delivery, pre-existing conditions such as hypertension and diabetes, and cervical dilation of less than 3 cm at onset. Based on these factors, a predictive model with a corrected bias concordance index of 0.709 was developed, indicating a moderate level of predictive accuracy within a 95% CI of 0.671–0.750. Jochum et al. [ 25 ] conducted a secondary analysis of data collected from a prospective, multicenter, observational cohort study in France, which led to the development of a scoring system based on factors such as height, BMI, gestational age, parity, cervical dilation, degree of cervical effacement, fetal head position, and indications for labor induction (including whether the induction was iatrogenic, suspicion of a macrosomic infant, premature membrane rupture, and suspicious fetal conditions). The AUCs for the developed model and its internal validation were 0.76 (0.73–0.79) and 0.74 (0.70–0.78), respectively. Chen et al. [ 26 ] conducted a prospective study on 150 nulliparous women who were clinically suspected of having cephalopelvic disproportion. They formulated a predictive model to assess the cesarean section risk for first-time mothers, considering the pre-delivery maternal BMI, distance between the femoral heads, obstetric conjugate, and fetal head and abdomen circumferences. The model’s performance, evaluated using the AUC, was 0.838, with a 95% CI of 0.774–0.902. The diagnostic accuracy of the model was reflected in the sensitivity and specificity values of 0.787 and 0.764, respectively, and the predictive values for positive and negative outcomes were 0.696 and 0.840, respectively. The calibration of the model was satisfactory with a calibration slope of 0.945, indicating a good fit between the predicted and observed probabilities, which are primarily applicable when predicting cesarean sections before the onset of labor. The improper application of these models may increase the likelihood of cesarean sections. Currently, an increasing number of obstetricians advocate allowing nulliparous women to have a full trial of vaginal delivery, making the assessment of abnormal labor progress and the appropriate timing of decisions for cesarean section particularly important. In this study, we assessed the AOP at the onset of the active phase in the first stage of labor, along with the use of oxytocin for induction, pre-delivery ultrasound measurements of fetal head circumference, ultrasound-estimated fetal weight, and the use of labor analgesia. Using logistic regression analysis, we developed a prospective predictive model tailored to low-risk full-term pregnant nulliparous women, focusing on conversion to cesarean sections. This model was further supported by the development of a nomogram designed to facilitate the visual and intuitive assessment of predictive factors. The efficacy of our predictive model was underscored by its robust AUC score of 0.924 on the ROC curve, with a 95% CI of 0.892–0.956, indicating excellent discriminatory power. This study had certain limitations. The modest sample size drawn from a single center may have affected the generalizability of the findings. To address this, future research should undertake multicenter validation to ascertain the precision of the model across diverse populations. Additionally, while ultrasound is an invaluable assessment tool during labor, imprecise measurements may inadvertently increase the incidence of unwarranted cesarean sections or unsuccessful instrumental deliveries. Consequently, our study underscores the importance of comprehensive training and rigorous performance evaluations by obstetricians in the application of transperineal ultrasound to ensure its effective and accurate use in clinical practice. Conclusion Our study conclusively demonstrated that specific predelivery ultrasound metrics, such as fetal head circumference, estimated fetal weight, and AOP, as well as the use of oxytocin for induction and labor analgesia, during the early active phase of labor, are critical predictors of the potential need for cesarean section in nulliparous women. The significance of AOP as a prognostic indicator of childbirth outcomes, particularly at the onset of the active phase of labor, has been further validated. This parameter introduces an additional dimension to the evaluation of labor progression, potentially mitigating complications arising from delayed cesarean sections. The predictive model we developed, integrating both ultrasound and clinical parameters, exhibited commendable predictive capabilities and precision. It provides a robust framework to guide clinicians in making informed decisions regarding the mode of delivery and optimal timing of cesarean sections. The application of this model must not be merely formulaic; it should be integrated with individual clinical acumen and tailored to the unique circumstances of each parturient. This approach ensures utmost consideration for the well-being and successful outcomes of both mothers and children. Declarations Acknowledgments We would like to thank Editage (www.editage.cn) for English language editing. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author contributions Zhanpeng Yu : conceptualization; writing-original draft preparation; writing-review & editing. Shunlan Du : methodology; project administration. Lili Xu : conceptualization; writing-review & editing. Yun Cheng : data curation; software. Feijun Hu : data curation; validation. Qiaoyang Xu : resources. All authors read and approved the final manuscript. Competing interests The authors declare no competing interests. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Wang, J. G., Sun, J. L. & Shen, J. Factors affecting failed trial of labor and countermeasures: a retrospective analysis. World J. Clin. Cases . 8 , 3483–3492 (2020). Yuan-ying, L. I. & Yong-qing, W. A. Research progress on influencing factors of intrapartum cesarean section in New Labor Standard. J. Int. Obstet. Gynecol. 48 , 481–485 (2021). Chen, G., Li, X. & Cai, W. 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Med 486–490 (2015). Shi, C. & Li, B. Expert consensus on new labor standards and management. Chin J. Obstet. Gynecol 486 (2014). Herlt, C., Stark, R., Sigmarsson, H. L. & Kauffold, J. Feasibility of transabdominal Doppler sonography for studying uterine blood flow characteristics in cycling gilts. Tierarztl. Prax Ausg G Grosstiere Nutztiere . 46 , 154–163 (2018). Jonker, L. & Memon, F. Influence of maternal factors and mode of induction on labour outcomes: a pragmatic retrospective cohort study. J. Obstet. Gynaecol. 38 , 946–949 (2018). Kohls, F. et al. Intrapartum translabial ultrasound: a systematic analysis of the fetal head station in the first stage of labor. Z. Geburtshilfe Neonatol . 222 , 19–24 (2018). Eggebø, T. M., Hassan, W. A., Salvesen, K. Å., Lindtjørn, E. & Lees, C. C. Sonographic prediction of vaginal delivery in prolonged labor: a two-center study. Ultrasound Obstet. Gynecol. 43 , 195–201 (2014). Carvalho Neto, R. H. et al. Assessment of the angle of progression and distance perineum-head in the prediction of type of delivery and duration of labor using intrapartum ultrasonography. J. Matern Fetal Neonatal Med. 34 , 2340–2348 (2021). Nelson, P. & Nugent, R. The association between sonographic fetal head circumference, obstetric anal sphincter injury and mode of delivery: a retrospective cohort study. Aust N Z. J. Obstet. Gynaecol. 61 , 722–727 (2021). Ejigu, A. G. & Lambyo, S. H. Predicting factors of failed induction of labor in three hospitals of Southwest Ethiopia: a cross-sectional study. BMC Pregnancy Childbirth . 21 , 387 (2021). Levine, L. D. et al. A validated calculator to estimate risk of cesarean after an induction of labor with an unfavorable cervix. Am. J. Obstet. Gynecol. 218 , 254e1–254e7 (2018). Kawakita, T. et al. Predicting vaginal delivery in nulliparous women undergoing induction of labor at term. Am. J. Perinatol. 35 , 660–668 (2018). Bergholt, T. et al. Maternal age and risk of cesarean section in women with induced labor at term-A Nordic register-based study. Acta Obstet. Gynecol. Scand. 99 , 283–289 (2020). Nwabuobi, C. et al. Risk factors for Cesarean delivery in pregnancy with small-for-gestational-age fetus undergoing induction of labor. Ultrasound Obstet. Gynecol. 55 , 799–805 (2020). Hikita, N. et al. Is high maternal body mass index associated with cesarean section delivery in Mongolia? A prospective observational study. Asian Pac. Isl Nurs. J. 4 , 128–134 (2019). Stark, E. L., Grobman, W. A. & Miller, E. S. The association between maternal race and ethnicity and risk factors for primary cesarean delivery in nulliparous women. Am. J. Perinatol. 38 , 350–356 (2021). Alzate, M. M., Dongarwar, D., Matas, J. L. & Salihu, H. M. Phenotypes and markers of cesarean delivery among Colombian women. Int. J. Gynaecol. Obstet. 147 , 187–194 (2019). Tolcher, M. C. et al. Predicting cesarean delivery after induction of labor among nulliparous women at term. Obstet. Gynecol. 126 , 1059–1068 (2015). Jochum, F. et al. Externally validated score to predict Cesarean delivery after labor induction with cervi ripening. Obstet. Gynecol. 134 , 502–510 (2019). Chen, C. et al. Magnetic resonance imaging-based nomogram to antenatal predict cesarean delivery for cephalopelvic disproportion in primiparous women. J. Magn. Reson. Imaging . 56 , 1145–1154 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6992403","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491787123,"identity":"c8db82be-411f-4d56-8138-8d7a46b87606","order_by":0,"name":"Zhanpeng Yu","email":"","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhanpeng","middleName":"","lastName":"Yu","suffix":""},{"id":491787124,"identity":"29c4972d-92a2-45aa-9bc6-434ab2739d69","order_by":1,"name":"Shunlan Du","email":"","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shunlan","middleName":"","lastName":"Du","suffix":""},{"id":491787125,"identity":"498e4570-ca9c-44a6-b266-2fd3c6b60c8f","order_by":2,"name":"Lili Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFAC5oYDDwzYePjZ2w8+SKioIUYLY8OBBAM+GcmeM8kGD84cI04LQwKDnI3BDAczyYctzIQ1GNxIbDyQUGDGYyDBkFaR2MDGwN/enYBXi+SMRJDD0njMpRuP3UjcIcMgcebsBrxa+CXAWo7xWM45kHYj8Qwbg4FELn4tbBAt/3kMbiSYFSS2MRPWArWFDayFgSgtkj0PIVpAgSyRcOYYD0G/GBxPPvzhwx82e1BUfvxRUSPH396LXwsG4CFN+SgYBaNgFIwCrAAAYWJM1GW8qBwAAAAASUVORK5CYII=","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Xu","suffix":""},{"id":491787128,"identity":"9a21c79f-7aea-4d76-8abb-b97209184a06","order_by":3,"name":"Yun Cheng","email":"","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Cheng","suffix":""},{"id":491787131,"identity":"c0c558ec-212a-4e68-91c1-2411519922e4","order_by":4,"name":"Feijun Hu","email":"","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feijun","middleName":"","lastName":"Hu","suffix":""},{"id":491787132,"identity":"e1ce9d36-b451-4b0b-91b3-89536e1d389a","order_by":5,"name":"Qiaoyang Xu","email":"","orcid":"","institution":"Dongyang Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiaoyang","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-06-27 14:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6992403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6992403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87881995,"identity":"4415bbca-d407-4733-83e4-053cd0d44b82","added_by":"auto","created_at":"2025-07-30 04:24:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":607514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement of AOP and HPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAOP: Angle between the long axis of the pubic symphysis and the transverse line passing through the lower edge of the pubic symphysis to the echoic ring of the fetal skull.\u003c/p\u003e\n\u003cp\u003eHPD: Distance between the lower edge of the pubic symphysis and the fetal skull along the subpubic line.\u003c/p\u003e\n\u003cp\u003eAOP: angle of progression; HDP: head-perineum distance\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/3495f1f6d60f53e1da83381f.png"},{"id":87882667,"identity":"b7f07d24-fa7a-4692-b2b1-b1ca033b1dc0","added_by":"auto","created_at":"2025-07-30 04:40:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":657588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic; DCA: decision curve analysis\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/2c348d77628fb33be0c4d711.png"},{"id":87882668,"identity":"875dbdc9-d976-48ae-96c4-afc6fbe13b28","added_by":"auto","created_at":"2025-07-30 04:40:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":573777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram of the cesarean section prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOI: oxytocin induction; Head: pre-delivery ultrasound measurement of fetal head circumference; UW: ultrasound estimation of fetal weight; AOP: angle of progression\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/c9cb25d98014a3b91e32e855.png"},{"id":87882242,"identity":"167ba4e6-f80b-4cf8-b1d7-494f0c14680b","added_by":"auto","created_at":"2025-07-30 04:32:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":349292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve of the predictive model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrain dataset: derivation cohort, Test dataset: validation cohort\u003c/p\u003e\n\u003cp\u003eTPR: true positive rate; AUC: area under the curve; FPR: false positive rate\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/f960a0cd21758984199ca2ad.png"},{"id":87882245,"identity":"3451a601-2a50-423b-9427-eceec821cb05","added_by":"auto","created_at":"2025-07-30 04:32:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":538300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the nomogram of the derivation cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/3a25df1be1ddbfcf7b56d447.png"},{"id":87882000,"identity":"0b721a88-86f9-4acc-bfb9-d6522c6cf70a","added_by":"auto","created_at":"2025-07-30 04:24:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":537033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the nomogram of the validation cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/b79da49d80951b102f648fc7.png"},{"id":87882248,"identity":"5a0e27ec-31c3-4ff9-8f19-68f7b5afdac8","added_by":"auto","created_at":"2025-07-30 04:32:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":553490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrain: derivation cohort, Test: validation cohort\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/8cabe829f66b66efe7a6501b.png"},{"id":97671973,"identity":"a0d68737-6cf0-4a03-8ae1-f87788f7e45f","added_by":"auto","created_at":"2025-12-08 09:33:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5181136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6992403/v1/f33079fa-b65c-4376-bacd-f464fa71b420.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Intrapartum Ultrasound-Based Predictive Model for Cesarean Section Conversion in Nulliparous Women Following Unsuccessful Vaginal Delivery Attempts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVaginal birth, which closely aligns with the natural course of human development, is widely recognized as the optimal mode of delivery, providing substantial benefits for maternal and infant health, facilitating postpartum recuperation, and promoting healthier outcomes in future pregnancies. Most expectant mothers prefer vaginal delivery. However, its unpredictability is influenced by several factors, including maternal and fetal conditions, strength of uterine contractions, dimensions of the birth canal, and emotional states. Among nulliparous women, the rate of cesarean section following an unsuccessful vaginal delivery was approximately 13.54% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. An unsuccessful trial increases the risk of serious complications, including postpartum hemorrhage, maternal infection, neonatal metabolic acidosis, and intracranial hemorrhage, posing long-term health risks to both mothers and children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. If labor abnormalities arise during the first stage, delaying a cesarean section can complicate the procedure and increase the likelihood of surgical and postsurgical complications such as bleeding, uterine incision damage, amniotic fluid infection, and neonatal injury. Therefore, timely detection of abnormalities in the first stage of labor, prompt intervention, choosing the appropriate time for cesarean section, and improving the quality of obstetric clinical practice have become urgent issues in the field of obstetrics. Conventional assessment of labor progression mainly depends on multiple vaginal examinations by medical staff to monitor cervical dilation and fetal descent, which may cause discomfort, infection, and genital tract trauma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Vaginal examination of cervical dilation and descent of the fetal presenting part is highly subjective and may not accurately identify the fetal position in some cases.\u003c/p\u003e\u003cp\u003eThe 2019 International Society of Ultrasound in Obstetrics and Gynecology guidelines for intrapartum ultrasound practice suggest that using ultrasound during labor management is increasingly recognized for its enhanced precision and reliability over traditional clinical assessments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research indicates that ultrasound can more accurately determine fetal head position and station, predict labor arrest, and potentially identify women who are likely to have a spontaneous vaginal birth compared with those who may require surgical intervention. Moreover, emerging evidence suggests that ultrasonography during labor can predict the success of instrumental vaginal delivery, providing a valuable tool for improving the prediction and management of childbirth outcomes. Many scholars have proposed models [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] based on the clinical parameters of pregnant women and fetuses, as well as prenatal ultrasound variables, to predict the risk of cesarean section in pregnant women with singleton full-term cephalic presentation. However, research on the application of intrapartum ultrasound measurements in predicting cesarean section during vaginal trials of labor in full-term low-risk nulliparous women is lacking. Therefore, this study aimed to construct a nomogram for predicting cesarean section during the onset of the active phase of the first stage of labor based on perineal ultrasound parameters (angle of progression [AOP] and head-perineum distance [HPD]) combined with factors such as maternal age, gestational age, body mass index (BMI), and the use of labor analgesia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy participants\u003c/h2\u003e\u003cp\u003eThis forward-looking cohort study enrolled 432 full-term, low-risk nulliparous women admitted for vaginal delivery trials at the Obstetrics Department of Dongyang Hospital, affiliated with Wenzhou Medical University, between June 2021 and January 2024. Participants were selected according to strict predefined inclusion and exclusion criteria to ensure a robust and representative sample.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were as follows: primiparous women in labor, full-term singleton fetuses in a cephalic presentation, fetal malformations excluded by prenatal check-ups, no pelvic structural abnormalities in pregnant women, no pregnancy complications or comorbidities, and no contraindications for vaginal delivery.\u003c/p\u003e\u003cp\u003eThe exclusion criteria were as follows: macrosomic infants (\u0026gt;\u0026thinsp;4000 g) or low-birthweight infants (\u0026lt;\u0026thinsp;2500 g), those who refused vaginal delivery trials or ultrasound examinations, and those who requested cesarean section without surgical indications.\u003c/p\u003e\u003cp\u003eAll examinations and procedures were conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the Dongyang People\u0026rsquo;s Hospital (DRY-2022-YX-066). The study was conducted strictly following the stipulated procedures, and all pregnant women provided written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePatient information\u003c/h3\u003e\n\u003cp\u003eThe following information was collected from patient medical records: mode of delivery, including spontaneous delivery, assisted vaginal delivery (including vacuum extraction, forceps delivery, and perineal laceration repair), and cesarean section; maternal age, height, BMI, gestational age, fundal height, abdominal circumference, and estimated fetal weight (fetal weight\u0026thinsp;=\u0026thinsp;fundal height \u0026times; abdominal circumference\u0026thinsp;\u0026plusmn;\u0026thinsp;200 g); prenatal ultrasound examination, including fetal head circumference, abdominal circumference, and estimated fetal weight (based on the last B-mode ultrasound result before delivery); induction methods (oxytocin, cervical balloon, and dinoprostone pessary induction; administration of labor analgesia; surgical indications for cesarean section conversion after failed vaginal delivery; and neonatal weight and Apgar score. Gestational age was verified by crown-rump length during early pregnancy.\u003c/p\u003e\n\u003ch3\u003eInduction methods\u003c/h3\u003e\n\u003cp\u003eMethods of induction were classified as no induction, oxytocin induction (induction using intravenous drip of oxytocin alone), cervical balloon induction (with or without oxytocin or artificial membrane rupture), and dinoprostone pessary induction (with or without oxytocin or artificial membrane rupture).\u003c/p\u003e\n\u003ch3\u003eCesarean section indications\u003c/h3\u003e\n\u003cp\u003eThe indications for cesarean section were fetal distress in utero, arrest of the active phase, and prolonged second stage of labor. The definitions are contained in the relevant guidelines and consensus documents [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Neonatal asphyxia was defined by an Apgar score of \u0026le;\u0026thinsp;7 at 1 or 5 min after birth.\u003c/p\u003e\n\u003ch3\u003eUltrasound imaging\u003c/h3\u003e\n\u003cp\u003eA Mindray Model 7 ultrasound diagnostic device was used in the second- and third-trimester modes with a convex array transducer, and the transducer frequency was 3.0\u0026ndash;5.0 MHz.\u003c/p\u003e\u003cp\u003eDuring the first stage of labor, when cervical dilation reached 4\u0026ndash;6 cm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the examination was initiated, and ultrasound measurements were taken between contractions. The pregnant woman was placed in the lithotomy position, the bladder was emptied, and the HPD and AOP were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two trained obstetricians performed the ultrasound examination using the same model as the portable ultrasound machine, employing the \u0026ldquo;blind method\u0026rdquo; to measure and record the ultrasound data of the pregnant women without sharing the results. Each ultrasound measurement was repeated twice, and the average value was considered the final result. A midwife with more than 3 years of work experience performed the vaginal examination, with the examiner present throughout, and results were not shared between the ultrasound and vaginal examiners.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData analysis and model development\u003c/h2\u003e\u003cp\u003eUsing the rms package in R version 4.1.1, we categorized parturients into two distinct cohorts based on their delivery method: those who delivered vaginally and those who underwent cesarean section. We analyzed the collected parturient data to delineate epidemiological profiles. Subsequently, a group-wise analysis was performed. To ensure the reliability of model validation, we used a random stratified sampling method to divide the entire dataset into a training set (n\u0026thinsp;=\u0026thinsp;346) and a validation set (n\u0026thinsp;=\u0026thinsp;86) in an 8:2 ratio. In the training set, a univariate analysis was initially conducted, followed by multivariate analysis using binary logistic regression. This approach was used to identify and quantify factors influencing the likelihood of cesarean section conversion. The regression model encompassed various statistical variables potentially affecting the delivery mode, with cesarean section conversion as the dependent variable and the previously listed factors as independent variables. We calculated the model\u0026rsquo;s discrimination (C-statistic) and calibration (Hosmer\u0026ndash;Lemeshow test). External validation was performed in the validation set to assess the model\u0026rsquo;s generalizability.\u003c/p\u003e\u003cp\u003eWe constructed a nomogram, a graphical representation designed for ease of clinical use, which assigns scores to each variable based on individual test results, with the aggregated score reflecting the estimated risk of cesarean section conversion.\u003c/p\u003e\u003cp\u003eThe validity of the model was ascertained through separate evaluations using both derivation and validation cohorts. We assessed the model\u0026rsquo;s discriminatory power (concordance index), its calibration (via calibration plots), and its diagnostic accuracy (via the receiver operating characteristics [ROC] curve) for each cohort. Subsequently, an overall evaluation of the efficacy of the model was conducted. Furthermore, we performed a decision curve analysis (DCA) to enhance our understanding of the model\u0026rsquo;s clinical utility.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStudy participants\u003c/h2\u003e\u003cp\u003eOut of the 432 low-risk nulliparous women with full-term singleton vertex fetuses who underwent vaginal delivery, 380 women successfully delivered vaginally (including eight forceps-assisted cases), and 52 women underwent a cesarean section, resulting in a cesarean delivery rate of 12.03%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares the demographic and clinical profiles of the two participant cohorts. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a visual representation of the study methodology as a flowchart illustrating the process from recruitment to data analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (Vaginal delivery)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (Cesarean delivery)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.50 [25.00, 31.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.00 [25.00, 31.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.00 [25.75, 31.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160.00 [155.00, 163.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160.00 [155.00, 163.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e158.00 [155.00, 161.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.75 [62.00, 76.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.30 [62.00, 76.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.50 [64.75, 77.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGes, weeks (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.00 [38.00, 40.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.00 [38.00, 40.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.00 [39.00, 40.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.23 [25.00, 29.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.07 [24.75, 29.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.80 [25.76, 29.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185 (42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 (57.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e247 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e273 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e247 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e159 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCbI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411 (95.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDpI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e427 (98.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor analgesia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220 (57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160 (42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (61.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead, cm (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e331.00 [324.00, 339.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e331.00 [324.00, 338.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e336.00 [329.75, 342.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAb, cm(median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e341.00 [331.00, 350.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e340.00 [331.00, 349.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e348.00 [338.75, 359.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFHw, g (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3303.50 [3031.50, 3498.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3283.00 [3013.00, 3484.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3469.50 [3213.75, 3661.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUw, g (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3366.00 [3165.00, 3640.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3333.00 [3128.00, 3600.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3613.00 [3392.00, 3821.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOP, \u0026deg; (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.20 [130.50, 144.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.15 [131.75, 144.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128.45 [123.18, 129.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPD, cm (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.89 [1.51, 2.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86 [1.45, 2.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.16 [1.99, 2.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eIQR: interquartile range; Ges: gestational weeks; BMI: body mass index; NI: no induction; OI: oxytocin induction; CbI: cervical balloon induction; DpI: dinoprostone pessary induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference; Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFetal distress, accounting for 46.2% of cesarean section conversions (24 of 52 cases), was the primary reason for switching from vaginal to cesarean delivery, followed by arrest of the active phase of labor (28.8%, 15 of 52 cases). A detailed breakdown of these indications is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndications for cesarean section after failed vaginal delivery (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndications for cesarean section\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetal distress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive phase arrest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrest of fetal head descent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProlonged second stage of labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlacental abruption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChorioamnionitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEstablishing the predictive model\u003c/h2\u003e\u003cp\u003eIn the univariate analysis, the 432 participants were categorized into two cohorts based on their vaginal delivery attempts: vaginal delivery and cesarean section. The analysis compared data pertinent to each group and found no statistically significant disparities in maternal age, gestational age, height, weight, predelivery BMI, and gestational age at the time of delivery among the nulliparous women in either cohort, irrespective of the induction method employed (none, cervical balloon, or dinoprostone pessary), with P-values exceeding the threshold of 0.05.\u003c/p\u003e\u003cp\u003eIn contrast, significant differences were observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) when evaluating predelivery ultrasound measurements, such as fetal head and abdominal circumferences and ultrasound-estimated fetal weight. Discrepancies were also observed in the estimated fetal weight derived from fundal height and abdominal circumference measurements, oxytocin use for induction, labor analgesia, AOP, and HPD values recorded at the onset of the active labor phase. A detailed examination of these findings is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of univariate logistic regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartial regression coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.980 (0.917, 1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.970 (0.918, 1.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012 (0.979, 1.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.236 (0.915, 1.669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.059 (0.972, 1.154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-19.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1256.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000 (0.000, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.187 (1.163, 4.113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCbI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e959.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e460395647.826 (0.000, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDpI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e727.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42551641.385 (0.000, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor analgesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.969 (1.040, 3.728)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.058 (1.024, 1.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.035 (1.012, 1.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFHw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002 (1.001, 1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002 (1.001, 1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.784 (0.730, 0.842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113.202 (21.896, 585.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eOR: odds ratio; CI: confidence interval; Ges: gestational weeks; BMI: body mass index; NI: no induction; OI: oxytocin induction; CbI: cervical balloon induction; DpI: dinoprostone pessary induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing the data of 432 nulliparous women, we performed a comprehensive multivariate analysis, incorporating variables that demonstrated statistical significance in the preceding univariate analysis. The binary outcome variable distinguished between successful vaginal delivery (coded as 0) and failed attempt resulting in cesarean section conversion (coded as 1). The findings revealed several independent predictors of cesarean section conversion, including the administration of oxytocin for labor induction, predelivery ultrasound measurements of fetal head circumference, ultrasound-estimated fetal weight, provision of labor analgesia, and AOP at the commencement of the active phase of labor, all with a P-value of less than 0.05. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of multivariate logistic regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartial regression coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.446 (1.360, 8.728)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLabor analgesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.500 (1.711, 11.836)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.079 (1.004, 1.159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.013 (0.936, 1.097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFHw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.999 (0.995, 1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002 (1.001,1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.760 (0.680, 0.850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.379 (0.225, 25.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eOR: odds ratio; CI: confidence interval; OI: oxytocin induction; Head: pre-delivery ultrasound measurement of fetal head circumference; Ab: pre-delivery ultrasound measurement of fetal abdominal circumference; FHw: estimation of fetal weight by fundal height and abdominal circumference; Uw: ultrasound estimation of fetal weight; AOP: angle of progression; HPD: head-perineum distance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe used the rms package in R software to develop a nomogram model that integrated the risk factors for cesarean section conversion identified through our multivariate logistic regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This nomogram assigns a specific score to each parameter for an individual nulliparous case based on variables associated with the risk of cesarean section conversion. By summing these individual scores, we obtained a total score for each woman, which we used to estimate the likelihood of cesarean section conversion via the total score axis on the nomogram.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe cesarean section conversion prediction model was evaluated as follows. The calculated values for the predictive model were utilized as the predictor variable, while the binary outcome of the cesarean section conversion served as the criterion variable. Through ROC curve analysis, we determined the model\u0026rsquo;s discriminative ability. For the group used to construct the model, the area under the ROC curve (AUC) was 0.924 (95% confidence interval [CI]: 0.892\u0026ndash;0.956). The maximum Youden index of the model was 0.714, corresponding to an optimal cutoff value of 0.099. This yielded a sensitivity of 0.933 and a specificity of 0.781. For the validation group, the AUC of the ROC curve was 0.916 (95% CI: 0.817\u0026ndash;1.000), and sensitivity and specificity were 0.857 and 0.827, respectively. A visual representation of these results is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The concordance indexes, which quantify the model\u0026rsquo;s overall correctness, were 0.92 for the derivation cohort and 0.91 for the validation cohort. The predicted probability of cesarean section during the labor trial showed good consistency with the actual observed probability (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDCA is a clear and accessible method for evaluating the practical clinical value and potential impact of predictive modeling tools. Our application of DCA to the nomogram developed in this study demonstrated its enhanced capacity to accurately forecast the likelihood of cesarean section conversion, offering valuable insights for clinical decision-making. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows a visual representation of the DCA findings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTraditionally, labor progression is monitored through repeated vaginal examinations by obstetricians and midwives. Being non-invasive, ultrasound examination offers substantial benefits in terms of safety and repeatability. This approach is widely accepted by pregnant women because of its minimal discomfort and capacity to prevent infections that can arise from multiple invasive procedures. Compared to the traditional conventional method of vaginal palpation, perineal ultrasound and other ultrasound-guided techniques provide greater precision in monitoring fetal head descent and cervical dilation. Quantifiable and consistent ultrasound methods allow a more objective evaluation, considerably reducing subjective interpretation by medical personnel, thereby enhancing the precision and impartiality of assessing labor progression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In this study, we prioritized perineal ultrasound parameters, specifically AOP and HPD, measured at the onset of the active phase in the first stage of labor, as pivotal determinants. These were considered alongside maternal demographics, including age, gestational age, BMI, and use of labor analgesia. Using logistic regression analysis, we developed a forward-looking predictive model tailored for low-risk full-term pregnancies in nulliparous women based on initial AOP and HPD values.\u003c/p\u003e\u003cp\u003eAdditionally, we developed a nomogram, a user-friendly graphical tool, to streamline personalized and methodical assessment of labor progress and forecast the risk of cesarean section conversion. This instrument is particularly beneficial for women who are at an elevated risk of requiring a cesarean section because it aids in the prompt detection of labor irregularities during the first stage. Early recognition of such abnormalities is crucial for avoiding delayed cesarean section, which is instrumental in averting negative outcomes for both mothers and children.\u003c/p\u003e\u003cp\u003eOur research identified key determinants that significantly influenced the likelihood of cesarean section conversion, including the use of oxytocin for labor induction, the initial AOP during the active phase of the first stage of labor, fetal head circumference as ascertained through predelivery ultrasound, fetal weight estimation derived from ultrasound, and administration of labor analgesia. These findings are consistent with the prevailing outcomes observed in related studies. The use of oxytocin for induction may affect the risk of conversion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering the accuracy and advantages of intrapartum ultrasound in assessing abnormal labor progress, our study specifically included the AOP value at the beginning of the active phase of the first stage of labor as one of the factors and concluded that the AOP value is a protective factor for cesarean section conversion. The larger the AOP value measured via ultrasound at the beginning of the active phase of the first stage of labor, the lower the probability of cesarean section conversion, which is similar to the results of previous research [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study determined the AOP cutoff value to be 125.6\u0026deg; using the ROC curve; a value less than 125.6\u0026deg; indicates a higher probability of cesarean section conversion. Eggeb\u0026oslash; et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that when the AOP was greater than 110\u0026deg;, the cesarean section rate was 12%, whereas when the AOP was less than 100\u0026deg;, the cesarean section rate increased to 62%. Some studies suggest that the risk of emergency cesarean section increases when ultrasound-estimated fetal weight is high [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Amini et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] showed that for every 1 cm increase in fetal head circumference, the odds ratio for the risk of cesarean section is 1.176 (95% CI: 1.112\u0026ndash;1.243). Ejigu et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported that the odds of failed induction were 4.3 times higher among women who were administered analgesia/anesthesia (adjusted odds ratio\u0026thinsp;=\u0026thinsp;4.37 [1.31\u0026ndash;14.59]) than among those who were not. These results are similar to those of this study. Levine et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] proposed in their study that maternal height is a protective factor for cesarean section conversion during vaginal delivery trials; the higher the height, the lower the probability of cesarean section conversion. Other researchers have suggested that indicators such as age, gestational age, and pre-pregnancy BMI are risk factors for cesarean section conversion [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; the older the age, the higher the pre-pregnancy BMI, and the greater the gestational age, the higher the probability of a cesarean section. Additionally, various factors such as ethnicity and social status can affect the final outcomes of childbirth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eScholars have already constructed predictive models for cesarean section conversion following failed vaginal delivery trials in full-term singleton vertex presentations among nulliparous women by combining multiple indicators. Tolcher et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] conducted a study involving 785 first-time mothers with single, full-term (at least 37 weeks), head-first pregnancies who were undergoing labor induction and identified several independent risk factors linked to a higher likelihood of cesarean delivery. These factors include maternal age, stature, BMI, weight gain during gestation, gestational age at the time of delivery, pre-existing conditions such as hypertension and diabetes, and cervical dilation of less than 3 cm at onset. Based on these factors, a predictive model with a corrected bias concordance index of 0.709 was developed, indicating a moderate level of predictive accuracy within a 95% CI of 0.671\u0026ndash;0.750. Jochum et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] conducted a secondary analysis of data collected from a prospective, multicenter, observational cohort study in France, which led to the development of a scoring system based on factors such as height, BMI, gestational age, parity, cervical dilation, degree of cervical effacement, fetal head position, and indications for labor induction (including whether the induction was iatrogenic, suspicion of a macrosomic infant, premature membrane rupture, and suspicious fetal conditions). The AUCs for the developed model and its internal validation were 0.76 (0.73\u0026ndash;0.79) and 0.74 (0.70\u0026ndash;0.78), respectively. Chen et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] conducted a prospective study on 150 nulliparous women who were clinically suspected of having cephalopelvic disproportion. They formulated a predictive model to assess the cesarean section risk for first-time mothers, considering the pre-delivery maternal BMI, distance between the femoral heads, obstetric conjugate, and fetal head and abdomen circumferences. The model\u0026rsquo;s performance, evaluated using the AUC, was 0.838, with a 95% CI of 0.774\u0026ndash;0.902. The diagnostic accuracy of the model was reflected in the sensitivity and specificity values of 0.787 and 0.764, respectively, and the predictive values for positive and negative outcomes were 0.696 and 0.840, respectively. The calibration of the model was satisfactory with a calibration slope of 0.945, indicating a good fit between the predicted and observed probabilities, which are primarily applicable when predicting cesarean sections before the onset of labor. The improper application of these models may increase the likelihood of cesarean sections. Currently, an increasing number of obstetricians advocate allowing nulliparous women to have a full trial of vaginal delivery, making the assessment of abnormal labor progress and the appropriate timing of decisions for cesarean section particularly important.\u003c/p\u003e\u003cp\u003eIn this study, we assessed the AOP at the onset of the active phase in the first stage of labor, along with the use of oxytocin for induction, pre-delivery ultrasound measurements of fetal head circumference, ultrasound-estimated fetal weight, and the use of labor analgesia. Using logistic regression analysis, we developed a prospective predictive model tailored to low-risk full-term pregnant nulliparous women, focusing on conversion to cesarean sections. This model was further supported by the development of a nomogram designed to facilitate the visual and intuitive assessment of predictive factors. The efficacy of our predictive model was underscored by its robust AUC score of 0.924 on the ROC curve, with a 95% CI of 0.892\u0026ndash;0.956, indicating excellent discriminatory power.\u003c/p\u003e\u003cp\u003eThis study had certain limitations. The modest sample size drawn from a single center may have affected the generalizability of the findings. To address this, future research should undertake multicenter validation to ascertain the precision of the model across diverse populations. Additionally, while ultrasound is an invaluable assessment tool during labor, imprecise measurements may inadvertently increase the incidence of unwarranted cesarean sections or unsuccessful instrumental deliveries. Consequently, our study underscores the importance of comprehensive training and rigorous performance evaluations by obstetricians in the application of transperineal ultrasound to ensure its effective and accurate use in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study conclusively demonstrated that specific predelivery ultrasound metrics, such as fetal head circumference, estimated fetal weight, and AOP, as well as the use of oxytocin for induction and labor analgesia, during the early active phase of labor, are critical predictors of the potential need for cesarean section in nulliparous women. The significance of AOP as a prognostic indicator of childbirth outcomes, particularly at the onset of the active phase of labor, has been further validated. This parameter introduces an additional dimension to the evaluation of labor progression, potentially mitigating complications arising from delayed cesarean sections.\u003c/p\u003e\u003cp\u003eThe predictive model we developed, integrating both ultrasound and clinical parameters, exhibited commendable predictive capabilities and precision. It provides a robust framework to guide clinicians in making informed decisions regarding the mode of delivery and optimal timing of cesarean sections. The application of this model must not be merely formulaic; it should be integrated with individual clinical acumen and tailored to the unique circumstances of each parturient. This approach ensures utmost consideration for the well-being and successful outcomes of both mothers and children.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.cn) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhanpeng Yu\u003c/strong\u003e: conceptualization; writing-original draft preparation; writing-review \u0026amp; editing. \u003cstrong\u003eShunlan Du\u003c/strong\u003e: methodology; project administration. \u003cstrong\u003eLili Xu\u003c/strong\u003e: conceptualization; writing-review \u0026amp; editing. \u003cstrong\u003eYun Cheng\u003c/strong\u003e: data curation; software. \u003cstrong\u003eFeijun Hu\u003c/strong\u003e: data curation; validation. \u003cstrong\u003eQiaoyang Xu\u003c/strong\u003e: resources. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, J. G., Sun, J. L. \u0026amp; Shen, J. Factors affecting failed trial of labor and countermeasures: a retrospective analysis. \u003cem\u003eWorld J. Clin. Cases\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 3483\u0026ndash;3492 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan-ying, L. I. \u0026amp; Yong-qing, W. A. 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Externally validated score to predict Cesarean delivery after labor induction with cervi ripening. \u003cem\u003eObstet. Gynecol.\u003c/em\u003e \u003cb\u003e134\u003c/b\u003e, 502\u0026ndash;510 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, C. et al. Magnetic resonance imaging-based nomogram to antenatal predict cesarean delivery for cephalopelvic disproportion in primiparous women. \u003cem\u003eJ. Magn. Reson. Imaging\u003c/em\u003e. \u003cb\u003e56\u003c/b\u003e, 1145\u0026ndash;1154 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"predictive model, intrapartum ultrasound, angle of progression, cesarean section conversion","lastPublishedDoi":"10.21203/rs.3.rs-6992403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6992403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to develop a predictive model for cesarean section conversion in nulliparous women using intrapartum ultrasound data. Low-risk nulliparous women carrying full-term singleton vertex fetuses were divided into derivation and validation cohorts. Ultrasound was used to measure the angle of progression and head-perineum distance for a cervical dilation of 4\u0026ndash;6 cm. Regression analyses identified factors affecting failed vaginal delivery trials and subsequent cesarean section conversion, and a risk prediction model was constructed. Independent predictors of cesarean section conversion included oxytocin use for induction, fetal head circumference, estimated fetal weight, labor analgesia, and angle of progression when the active labor phase commenced. 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\u0026ndash;0.956) and 0.916 (95% confidence interval: 0.817\u0026ndash;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. 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 assist clinicians in the choice of the appropriate mode of delivery, thereby improving maternal and infant outcomes.\u003c/p\u003e","manuscriptTitle":"An Intrapartum Ultrasound-Based Predictive Model for Cesarean Section Conversion in Nulliparous Women Following Unsuccessful Vaginal Delivery Attempts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 04:23:59","doi":"10.21203/rs.3.rs-6992403/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"602a56d1-23ad-4bcd-b8eb-9a3a38ad8148","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52207621,"name":"Health sciences/Diseases"},{"id":52207622,"name":"Health sciences/Health care"},{"id":52207623,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-12-05T12:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 04:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6992403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6992403","identity":"rs-6992403","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}