Participants
We conducted a retrospective cohort 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 on a sample of 6,551 women who delivered at the First Affiliated Hospital of Soochow University between January 1, 2011, and August 31, 2017. External validation of the prediction model was then performed using the data from Sihong county People’s Hospital between January 1, 2013, to December 31, 2019, to ensure the model has the same predictive ability as in the derivation cohort.
Institutional Review Board approval by these two hospitals was obtained for the study waiving informed consent for this retrospective study. Methods and reporting guidelines were followed by the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement[15] (Additional files 1).
Inclusion and exclusion criteria
Low-risk pregnancy nulliparous women undergoing the labor with singleton, term(37 0/7 weeks of gestation or greater) and cephalic pregnancies were recruited. Women had antepartum intrauterine fetal death or fetal anomalies were excluded. Exclusion criteria were as follows:
(1) 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);
(2) Women had a scarred uterus(e.g., myomectomy);
(3) Women had contraindications to vaginal delivery;
(4) Women had the cesarean delivery on maternal request.
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 collection
Data on maternal characteristics and perinatal parameters were collected from the institution’s obstetrics database, which was obtained by the patient’s medical record review.
Characteristics
The outcome of interest was defined as cesarean delivery. A cesarean delivery was performed if there was fetal distress, arrested active phase, prolonged latent phase, prolonged second stage, arrested descent, suspected chorioamnionitis, and other medical indications, such as threatened uterine rupture. It is worth noting that we only collected the major indications. The candidate predictor variables had to be easily accessible through characteristics’ data. To identify predictor variables, a systematic review of the literature was conducted[7, 11-14, 16-18]. The following variables were recorded: maternal age, height, weight, baseline body mass index (BMI), weight change during pregnancy, gestational age at delivery, premature rupture of membranes (PROM), epidural analgesia, meconium-stained amniotic fluid, intervention measures(oxytocin, amniotomy, disposable cervical dilator balloon, prostaglandin(Propess or Misoprostol)), neonatal sex, and neonatal birth weight.
Operational definitions
The relevant guidelines[19, 20] were used to determine cesarean delivery indications such as arrest of descent and a prolonged second stage of labor. BMI was calculated as weight (kg)/[height (m)]2. Baseline BMI was defined as pre-pregnancy BMI. Gestational age was calculated by the date of the last menstrual period and confirmed by ultrasound examination during first-trimester (by measuring the crown-rump length) or second-trimester(by measuring biparietal diameter, abdominal circumference and femur length).
We analyzed the labor process of each participant. They were divided into the non-intervention group and the intervention group according to whether they received intervention measures. These intervention measures included the use of oxytocin, amniotomy, disposable cervical dilator balloon and prostaglandin. The non-intervention group was defined as women who entered labor naturally and the labor process did not be intervened. The intervention group was divided into the augmentation subgroup and induction subgroup according to whether the cervix of the woman was dilated by 6 cm. The cut-off point of 6 cm was chosen because contemporary labor data indicated that active labor starts at 6 cm of cervical dilation[21]. The augmentation subgroup referred to women who received intervention measures such as oxytocin augmentation and amniotomy when their cervical dilation was greater than or equal to 6 cm. The induction subgroup referred to women who received intervention measures when their cervical dilation was less than 6 cm. We used the following modes to group the induction subgroup.
(1) Oxytocin Induction group: women only received oxytocin induction. This was defined as Induction method 1.
(2) Amniotomy group: women received artificial rupture of membranes or both artificial rupture of membranes and oxytocin induction(amniotomy after using Prostaglandin E2 or Disposable cervical dilator balloon was not included). This was defined as Induction method 2.
(3) Disposable Cervical Dilator Balloon: women received Disposable cervical dilator balloon induction. This was defined as Induction method 3.
(4) Prostaglandin E2 group: women received Prostaglandin E2(Propess) induction. This was defined as Induction method 4.
Considering that the women in the augmentation subgroup naturally entered the active stage of labor, the augmentation subgroup and the non-intervention group were combined into one group, which serves as a reference group for the induction subgroup. For convenience, spontaneous labor group was named this combined group.
Statistical analysis
Data analysis was conducted by using the statistical software package SPSS (24.0) and R (3.6.2).
Univariate analysis was performed for all clinical data. For continuous variables, the tests of normality were performed first. The Student’s t-test was used to compare the continuous variables with a normal distribution. The Mann-Whitney test was used to compare discrete or continuous variables without a normal distribution. The chi-square test and Fisher exact test were used, as appropriate, for the categorical variables. Standard descriptive statistics (mean ± standard deviations or median and interquartile range) were used to summarize continuous variables. Percentages and frequencies were used for categorical variables.
Baseline variables that were considered clinically relevant or that showed a univariate relationship with the outcome(candidate variables with a p value <0.05 on the univariate analysis) were entered into the multivariable logistic regression model. Variables for inclusion were carefully chosen, given the number of events available, to ensure parsimony of the final model. The results of logistic regression models were presented as odds ratio (OR) with their 95% confidence intervals (CIs).
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. The area under the receiver operating characteristic curve(AUC ROC) was calculated to assess the discrimination ability. AUC ROC was interpreted using following categories: non-informative (AUC ROC = 0.5), poor accuracy (0.5 < AUC ROC <0.7), moderate accuracy (0.7 < AUC ROC <0.9), high accuracy (0.9 < AUC ROC < 1); and perfect accuracy (AUC ROC = 1)[22]. The calibration of the prediction model was assessed using the Hosmer-Lemeshow goodness-of-fit test(P>0.05 was taken to indicate good fitting) and/or calibration plot. The ideal curve would be a 45-degree straight line. Perfect model calibration is represented graphically by a slope of 1 and an intercept of 0. Decision curve analysis (DCA) was used to assess the clinical value of the model, which was a method for evaluating the net benefit. A decision curve was plotted to inform clinicians about a range of threshold probabilities in which the models would be of clinical value once deployed in clinical practice. We assessed internal validity with a bootstrapping technique (resample 1000 times) to show the performance of model. We conducted an external verification of the final model in another hospital. We reported the predictive performance in the validation cohort also using the measures of discrimination and calibration. Based on the final model, we created a graphic nomogram that represented the predictive model by R. Established a dynamic nomogram by using the rms DynNom, and built a web online applications through shinyapps. All P values were two-tailed, and a significance level of 5% was used.