We conducted a retrospective cohorts study of low-risk nulliparous women with singleton, term, cephalic pregnancies who delivered at the Hospital of the First Affiliated Hospital of Soochow University and Sihong county People’s Hospital. The former hospital is a tertiary referral center, while the latter is a secondary referral center. There were two distinct phases to the overall study, the development and the validation phases. The prediction model was developed among women who delivered at the First Affiliated Hospital of Soochow University between January 1, 2011, and August 31, 2017. External validation was conducted using data from Sihong county People’s Hospital between January 1, 2013, and December 31, 2019. This was done to confirm that the model has consistent predictive ability outside of the original study population.

This study obtained Institutional Review Board approval from both hospitals, which waived the need for informed consent due to the retrospective nature of the study. The study adhered to the methods and reporting guidelines outlined in the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) statement [15].

We recruited nulliparous women with low-risk pregnancies, undergoing labor with singleton cephalic pregnancies at term (37 0/7 weeks of gestation or greater). Women had antepartum intrauterine fetal death or fetal anomalies were excluded. Exclusion criteria were as follows:

(i) Women had complications during pregnancy (e.g., cardiac failure, severe liver and kidney diseases, hypertensive disorders of pregnancy, diabetes, oligohydramnios, placenta previa, vasa previa and fetal growth restriction); (ii) Women had a scarred uterus (e.g., myomectomy); (iii) Women had contraindications to vaginal delivery; (iv) Women had the cesarean delivery on maternal request; (v) Women entered labor naturally or received intervention measures such as oxytocin augmentation and amniotomy when their cervical dilation was greater than or equal to 6 cm.

The pregnant women who meet the criteria were divided into the intrapartum cesarean delivery group and the vaginal delivery group according to their delivery modes.

Data on maternal characteristics and perinatal parameters were collected by reviewing the patient’s medical records from the obstetrics database maintained by the institution.

Continuous variables were assessed for normality before comparison. The Student’s t-test was used to compare normally distributed continuous variables, while the Mann-Whitney test was used for non-normally distributed or discrete variables. For categorical variables, we used the chi-square test or Fisher exact test, depending on the sample size. Descriptive statistics such as mean and standard deviations or median and interquartile range were used for continuous variables, while percentages and frequencies were used for categorical variables. We used the statistical software package SPSS (24.0, IBM Corp., Chicago, IL, USA) and R (3.6.3, R Foundation for Statistical Computing, Vienna, Austria) for data analysis.

Variables that had a p-value 0.10 on univariate analysis or were deemed clinically significant were entered into the multivariable stepwise forward logistic regression model. The logistic regression models were used to calculate odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) to present the results.

The discrimination and calibration of the prediction model were evaluated. Discrimination is the extent to which patients with cesarean delivery is identified likely to have this positive outcome. Calibration refers to the extent to which the calculated risks reflect the actual percentage of women with the outcome in each group. It is the agreement between observed outcomes and predictions. Discrimination is assessed by measuring the area under the receiver operating characteristic curve (AUC ROC), where an AUC of 0.5 indicates a non-informative model, while an AUC between 0.5 and 0.7 indicates poor discrimination, an AUC between 0.7 and 0.8 indicates acceptable discrimination, an AUC between 0.8 and 0.9 indicates excellent discrimination, and an AUC greater than 0.9 indicates outstanding discrimination [22]. The calibration of the prediction model was assessed using the Hosmer-Lemeshow goodness-of-fit test (a non-significant p-value indicates good calibration) and/or calibration plot (the ideal curve should be a 45-degree straight line). Additionally, a perfect calibration model would have a slope of 1 and an intercept of 0 on the calibration plot. Decision curve analysis (DCA) was utilized to evaluate the clinical utility of the model by assessing the net benefit. The DCA involved plotting a decision curve to provide clinicians with information about the range of threshold probabilities in which the model would be clinically valuable when implemented in clinical practice. To demonstrate the performance of the model, we evaluated its internal validity using a bootstrapping technique with 1000 resamples. We conducted an external verification of the final model in another hospital. In the validation cohort, we assessed the predictive performance of the model using the same measures of discrimination and calibration as in the development cohort. We also created a graphical nomogram based on the final model using R, and established a dynamic nomogram using the rms DynNom package. Additionally, we developed a web-based application through shinyapps. All statistical tests were two-tailed, and a significance level of 5% was used.

Results of the internal validation using bootstrap method indicated that the cesarean delivery rates predicted by both models were consistent with the real data (Fig. 2A,B).

Fig. 2.

Internal and external validation and predictive performance. (A,B) Calibration curves of the two models using 1000 bootstrap re-samples. (C,D) The red ROC curve represents data from the Model 1 and bule ROC curve represents data from the Model 2. (E,F) Calibration curves of the two models in the external validation set.

We validated the two formulas by using a separate data set which was derived from another hospital (Sihong county People’s Hospital).

To validate the logistic regression equations, an external cohort of 3421 low-risk nulliparous women who met the same inclusion criteria as those in the original dataset was used (Supplementary Table 1). It is worth noting that cervical dilator balloon and Propess were not routinely used as induction methods in Sihong county People’s Hospital in this study. In this hospital, 25 micrograms of misoprostol was used prostaglandin for induction of cervical ripening. Considering that misoprostol is also a prostaglandin drug induction method, therefore, when we verified externally, it replaced Propess in induction method. When these two formulas were applied to this cohort, it achieved AUC of 0.7576 (95% CI: 0.7311–0.7840) and 0.7578 (95% CI: 0.7313–0.7842) (Fig. 2C,D). The calibration plots presented an acceptable agreement in the validation cohort between the prediction and actual observation for the Model 1 and Model 2 (Fig. 2E,F).

A graphic nomogram was constructed to visualize the logistic regression model, which allows for the calculation of an individualized risk for intrapartum cesarean delivery (Fig. 3A,B). We also developed a friendly software-based calculator that can give the percentage likelihood of cesarean delivery. It can be found at: https://fangcan.shinyapps.io/Nomogramsex/, https://fangcan.shinyapps.io/nomogramnosex/.

Fig. 3.

The nomograms of the two models. (A) The nomogram for Model 1 (B) The nomogram for Model 2.

Intrapartum cesarean delivery has been concerned by many researchers. This study was aiming to identify the predictors for cesarean experience among low-risk women and develop a clinical prediction model. The model allows physicians to assess the individual and systematic risks of pregnant women before childbirth. It can be used during counseling to increase acceptance of vaginal delivery for those with a high chance of success and to minimize procedures performed on women with low chances for a successful vaginal delivery. Screening for high-risk women and applying appropriate interventions may reduce the risk of adverse outcomes. To achieve the best outcomes for mothers and babies, medical staff need to provide the pregnant women with the proper suggestion of delivery.

The model highlight the importance of variables such as maternal age, maternal height, baseline BMI, gestational age, weight gain during pregnancy, degree of meconium-stained amniotic fluid, indicator for occurrence of premature rupture of membranes and male fetus in determining the cesarean delivery when women failed the trial of labor. These results are consistent with the existing literature on the risk of cesarean delivery [18, 24, 25, 26]. Past studies also found gestational age of induced labor and induction methods were related to the incidence of caesarean [27, 28]. The ethnic disparity was observed in cesarean delivery rates, Stark et al. [29] showed that the frequency of cesarean was lowest in non-Hispanic white women and highest in non-Hispanic black women. In addition, delivery time, clinicians’ personal beliefs and pregnant women themselves’ views of decision-making also have an impact on delivery mode [30, 31, 32].

Currently, given the maternal and fetal implications of intrapartum cesarean, a number of prediction models aimed at determining the likelihood of cesarean delivery had been developed. A study by Levine et al. [11] used a nomogram to develop and validate a predictive model for women undergoing an induction of labor with an unfavorable cervix. Nulliparity, BMI, gestation age $\ge$40 weeks, modified Bishop score, and height were significantly associated with cesarean. This model with an AUC of 0.79 in the development cohort and 0.73 in the validation cohort. Jochum et al. [12] had developed a scoring system for predicting cesarean delivery after labor induction with cervical ripening based on a secondary data analysis. Height, BMI, gestational age, parity, dilation, effacement, fetal head station, medical indication, suspicion of macrosomia, PROM and concerning fetal status were found to be strongly associated with cesarean delivery. The AUC ROC in the derivation set and internal validation set were 0.76 and 0.74, respectively. Rossi et al. [13] developed and validated a predictive risk calculator for cesarean among women undergoing induction of labor. Seven independent risk factors were found to be associated with an increased risk of cesarean delivery, including prior vaginal delivery, maternal weight at delivery, height, age, prior cesarean delivery, gestational age at induction, and maternal race. The model had an AUC ROC of 0.787 (95% CI: 0.786–0.788), and it performed well on external validation (0.783, 95% CI: 0.764–0.802). Kamel et al. [14] developed and validated a model for the prediction of cesarean for failure to progress. The four independent predictive variables included maternal age, angle of progression at rest, cervical length and occiput posterior position. The calculated AUC ROC were 0.7969 (95% CI: 0.71–0.87) and 0.88 (95% CI: 0.79–0.97) for the developed and validated models, respectively.

The applicability of these models to Chinese women is uncertain. At the same time, China is a yellow race and people are relatively thin and have a low BMI. To date, China lacks the studies in relevant researches. Therefore, based on the analysis of clinically relevant factors of existing prediction models and the Fertility Policy of China (fetal sex identification is prohibited), this study established risk prediction models suitable for low-risk pregnant women for cesarean delivery through the multivariable Logistic regression analysis. These prediction models discriminated well (Model 1: AUC was 0.755, Model 2: AUC was 0.748). Moreover, an external validation of women from another hospital was conducted, which demonstrated a consistent measure of discrimination with the AUC of 0.7576 (95% CI: 0.7311–0.7840) and 0.7578 (95% CI: 0.7313–0.7842), respectively. The results of external validation showed that our prediction model can be further extended to the data set of another research center, indicating that the application value of the model has been greatly expanded. The online web server was constructed based on the nomogram to facilitate clinical practice. Clinicians can carry out a risk assessment and provide appropriate suggestion on patients at any time through mobile phones.

To our knowledge, this is the first study to develop a model for predicting the likelihood of cesarean delivery in low-risk Chinese nulliparous women using maternal and fetal factors. These models still show good prediction ability in both internal and external validation populations. In particular, we performed external validation of the model using a separate dataset from another hospital in China, which was lacking in many studies. In addition, based on the statistical analysis, the nomograms and the software-based calculators were also created for individual risk assessment.

The limitation of this study may include the following aspects. Firstly, this study has a retrospective design. In addition, the definition of indications for intrapartum cesarean was not completely consistent. The study [33] found that the frequency of various indications for cesarean delivery changed over time. The decline in the rate of labor arrest widely exceeding the increase in the rate of nonreassuring fetal status. In settings where maternal age is higher, in vitro fertilization conception more common and local thresholds for intervention due to obstructed labor or non reassuring fetal status are different, the performance of their models may be different. In fact, it is difficult to unify or standardize labor standard in clinical practice. There will be individual differences among women and different interventions in labor. At the same time, the indication of intrapartum cesarean during labor is not only related to the progress of labor but also affected by pregnant women, fetuses, and medical staff. Then, the sample size of the study was relatively small, and the included subjects were only the single-centered population in the region. The selection of the population also targeted only low-risk nulliparous. Given the diversity of geography, economy, medical level and environment throughout China, our findings may not representative the Chinese population in many jurisdictions. Finally, the neonatal birth weight had been associated with an increased risk of cesarean, but it was not included in the final prediction model because it could not be accurately known before delivery. Further investigation of model validity and impact before generalizing is important and should be undertaken.