Construction and verification of prediction models for intrapartum cesarean section to be used at full-term pregnant women with singleton and cephalic presentation under the new labor standard: a retrospective case-control study

doi:10.21203/rs.3.rs-2216237/v1

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Construction and verification of prediction models for intrapartum cesarean section to be used at full-term pregnant women with singleton and cephalic presentation under the new labor standard: a retrospective case-control study

https://doi.org/10.21203/rs.3.rs-2216237/v1

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Purpose:

This study aimed to explore the risk factors associated with intrapartum cesarean section and construct prediction models for intrapartum cesarean section under the new labor standard.

Methods:

This was a retrospective case-control study at Peking University Third Hospital in China from June 2020 to October 2021. Full-term pregnant women were allowed to have a trial of labor, including singleton and cephalic presentation. Herein, a total of 20 parameters relevant to maternal and obstetric characteristics were listed as candidate predictors. In addition, univariate and multivariate logistic regression analyses were used to construct the prediction model. Two prediction models were included: Model A is for the gravida before the trial of labor and model B is for those already at the active phase. Then, the calibration, discrimination, and clinical utility of the models was assessed and an internal validation was performed. Finally, the models were ultimately transformed into nomograms for clinical use.

Results:

The predictors in model A were older maternal age, shorter in height, longer gestational age, heavier in weight, primipara, lower Bishop score, complicated hypertensive disorder, receiving labor induction and heavier estimation of fetal weight(EFW) within one week before delivery. In model B, height, weight gain during pregnancy, complicated hypertensive disorder, receiving labor induction, analgesia, assist reproductive technology, latent period duration,delivery at night and EFW were included. The models showed satisfactory discrimination, calibration, and high clinical net benefit.

Conclusion:

The prediction models for an intrapartum cesarean section can accurately predict the risk of cesarean section; This prediction model can provide guidance for clinicians in the management of labor, grasp the appropriate indications for cesarean section, avoid excessive trial labor, and reduce maternal and infant complications.

Trial of labor

Intrapartum cesarean section

Prediction model

New labor standard

Risk factors

Intrapartum cesarean section increase the risk of postoperative complications. Our prediction model for intrapartum cesarean section can be used before the onset of labour and during the active phase to provide an individualized assessment and consultation to help clinicians to optimize the management during labor and reduce postpartum complications under the New Labor Standard.

A cesarean section is an effective intervention to resolve dystocia. Meanwhile, perinatal mortality and maternal and infant complications do not decrease as the cesarean section rate increases, on the contrary, the risk increases when the cesarean section rate exceeds 10–20%^[1]. According to the World Health Organization (WHO), the cesarean section rate should be controlled at 10–15%^[2]. Nevertheless, the rate of cesarean section in China has increased gradually from 28.8–36.7% between 2008 and 2018^[3].

In 2010, Zhang et al revisited the labor curves, which had replaced the labor curve of Friedman, and now conducting the modern obstetric practice^[4]. In 2014, based on a combination of Zhang's labor curve、characteristics specific to the Chinese population and guidelines issued by other countries, the obstetrics section of the Obstetrics and Gynecology branch of the Chinese Medical Association published “The expert consensus on the standards and treatment of new labor procedures”^[5]. This consensus aimed to promote vaginal delivery and reduce the cesarean section rate.

Moreover, the implementation of new labor procedures has increased the rate of vaginal deliveries and decreased the cesarean section rate. Except for this, the indications of the cesarean section have changed, cesarean section indicated by abnormal labor process and relative cephalopelvic disproportion decreased, however, fetal distress, intrapartum fever, and intrauterine infection increased^[6]. Meanwhile, Hadar indicated that intrapartum cesarean section is an independent risk factor for postoperative complications^[7]. Hence, those who underwent intrapartum cesarean section were more likely to sustain intraoperative trauma and postoperative complications as compared with those who undergo an elective cesarean section.

Regarding the potential hazards caused by intrapartum cesarean section, it would be beneficial to forecast the risk of an intrapartum cesarean section before the onset of labor and during the labor process. Several studies have developed prediction models for the outcome of induction of labor, however, none of the prediction models are widely implemented in clinical practice. Therefore, we performed a retrospective study to develop a prediction model for intrapartum cesarean section, hoping to conduct an individualized evaluation before delivery to optimize the management during labor and reduce postpartum complications.

This was a retrospective case-control study. Herein, we study women with singleton pregnancies, and cephalic presentation at term who were admitted to have a trail of labor in the Department of Obstetrics and Gynecology, Peking University Third Hospital, China from June 2020 to October 2021. The exclusion criteria were women with a uterine scar, complete placenta previa, cephalopelvic disproportion, genital malformation, and stillbirth. Women with positive oxytocin challenge test (OCT) or severe comorbidities and complications that cannot tolerate trial of labor were also excluded. After inclusion and exclusion criteria, the data in June and before June 2020 was used as the derivation data set, and data from July was analyzed as an internal validation set. This study was approved by the Ethics Committee of Peking University Third Hospital (ethical number:2021-173-01)

Herein, baseline maternal and obstetric characteristics including maternal age, height, pre-pregnancy body mass index(BMI), gestational age, parity, pregnancy complications, whether or not received assisted reproductive technology, labor induction, analgesia, premature rupture of membranes, Bishop score before induction of labor, amniotic fluid index (AFI), estimation of fetal weight measured by ultrasonography within one week before delivery, and duration of the latent period were recorded. Data on indications for intrapartum cesarean section were also collected.

The primary outcome for the prediction model was the risk of an intrapartum cesarean section who have a trial of labor. Statistical analyses were performed with the use of R software, version 3.0. Then, a univariate analysis of potential predictive factors was carried out using univariate logistic regression. Variables with a P value < 0.1 in univariate analysis were chosen to be included in the multivariate binary logistic regression model. Hence, this model was constructed using stepwise regression based on the Akaike Information Criterion (AIC) to select the optimal fitting model. The Hosmer-Lemeshow goodness of fit test and Brier score was used to evaluate the adequacy of calibration. Thus, a P value of ＞0.05 suggested a good calibration. A Brier score of zero represents a perfect model and scores of 0.25 or greater signal a non-informative model. Moreover, the discrimination of the model was assessed based on the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Meanwhile, an AUC greater than 0.75 indicates a model with a favorable prediction value.Furthermore, the multivariate logistic regression model was used to develop a nomogram. A decision curve analysis (DCA) was plotted to determine the clinical utility of the nomogram. Meanwhile, the discrimination and calibration of the nomogram were evaluated using a calibration curve. Hence, a two-sided p < 0.05 was considered statistically significant.

There were two prediction models included: Model A is for the gravida who decides to have a trial of labor and is admitted to the delivery room, and model B predicts the outcome of the woman who is already at the active phase.

After applying the inclusion and exclusion criteria, a total of 4,475 women were included in this study, of which 3,313 (74.03%) women were used as the training derivation set to construct the prediction model. Among them, 446 (13.46%) women transfer to the cesarean section after a trial of labor. Figure 1 shows the indication of intrapartum cesarean section. As indicated, fetal distress was the most common cause of intrapartum cesarean section under the new standard labor protocol.

Table 1 shows the maternal and obstetric characteristics. Women who delivered by intrapartum cesarean section with a failed vaginal delivery were more likely to be older, have longer gestational age, and have lower Bishop scores as compared with those who underwent vaginal labor. They were also more likely to be primiparous and had a more complicated proportion of pregestational and gestational diabetes mellitus and hypertensive disorder during pregnancy. In addition, the estimation of fetal weight (EFW) measured by ultrasonography within one week before delivery was higher in the intrapartum cesarean section than those who underwent vaginal labor. For women who already entered the active phase, their latent phase durations were much longer in the intrapartum cesarean section than that in the vaginal labor group.

Results of the multivariate regression analysis using the training cohort are shown in Table 2. In model A, nine factors including primipara, EFW measured by ultrasonography within one week before delivery, height, maternal age, gestational age, weight at delivery, Bishop score before the trial of labor, receiving labor induction, complicated hypertensive disorder of pregnancy were considered as the potential individual risk factor for intrapartum cesarean section and were used to develop the prediction model. Meanwhile, in model B, the Bishop score was excluded and latent phase durations, undergoing analgesia, assisted reproductive technology were included as prediction parameters. The ROC curve for predictive model A achieved an AUC of 0.804 (95% CI 0.782–0.824) in the training set and 0.778 (95% CI 0.732–0.822) in the validation samples, respectively (Fig. 2a,b). The AUC of model B was 0.862(95%CI 0.837–0.887)in the training set and 0.795(95%CI 0.727–0.864) in the validation samples(Fig. 3a,b). Meanwhile, the P value of the Hosmer-Lemeshow test in models A and B was 0.94 and 0.02, the Brier score in model A and model B were 0.09 and 0.04 respectively, which were by an acceptable calibration. As the prediction model showed satisfactory predictive ability, a nomogram with the parameters was plotted to predict an individualized patient risk assessment for intrapartum cesarean section at term, as shown in Fig. 4.The calibration curve of the nomogram showed good consistency between prediction and actual intrapartum cesarean section, as also shown in Fig. 2c,2d,3c,3d. Meanwhile, DCA curves in both the training and validation sets are shown in Fig. 2e,2f,3e,3f.

Table 1

Baseline characteristics of all patients in the training cohort and validation cohort
Characteristic	Training cohort(n = 3313) M (P25, P75)/N (%)							P value
Characteristic	Vaginal Delivery group(N = 2867)	Intrapartum cesarean section group(N = 446)	Total	P value	Vaginal Delivery group(N = 1050)	Intrapartum cesarean section group(N = 112)	Total	P value
Maternal age	32.40 ± 3.66	32.54 ± 3.49	32.42 ± 3.64	< 0.001	32.61 ± 3.73	33.24 ± 3.67	32.67 (3.73)	0.032
Height	1.63 ± 0.05	1.61 ± 0.05	162.85 ± 5.07	0.069	162.99 ± 5.17	160.32 ± 4.61	162.73 (5.18)	0.441
Prepregnant weight	57.54 ± 8.69	58.10 ± 9.03	57.62 ± 8.73	0.7	58.33 ± 8.78	58.75 ± 10.69	58.37 (8.98)	0.028
Weight at delivery	70.45 ± 9.19	71.75 ± 9.64	70.62 ± 9.26	0.042	70.59 ± 9.57	71.08 ± 10.89	70.64 (9.70)	0.601
Weight Gain during Pregnancy	12.90 ± 4.38	13.65 ± 4.47	13.00 ± 4.40		12.26 ± 4.25	12.33 ± 4.37	12.27 (4.26)	< 0.001
Preprgnancy BMI	21.61 ± 3.02	22.36 ± 3.21	21.71 ± 3.05	0.6	21.93 ± 2.9	22.81 ± 3.69	22.02 (3.02)	0.003
assisted reproductive technology	14% (405/2867)	25% (110 / 446)	515/3,313(16%)	0.2	229 / 1,050 (22%)	37 / 112 (33%)	266/1,162(23%)	< 0.001
primiparity	73% (2081/2867)	97%(433 / 446 )	2,514/3,313 (76%)	< 0.001	821 / 1,050 (78%)	107 / 112 (96%)	928/1,162 (80%)	0.006
gestational age	276.38 ± 7.06	279.75 ± 7.04	276.83 ± 7.15	< 0.001	277.06 ± 7.13	279.91 ± 7.24	277.33 (7.19)	0.016
premature rupture of membranes	28% (808/2867)	27%(122/446 )	930/3,313 (28%)	0.5	278 / 1,050 (26%)	20 / 112 (18%)	298/1,162 (26%)	0.111
gestational diabetes mellitus or pregestational diabetes mellitus	22% (630/2867)	25% (112/446)	742/3,313 (22%)	0.047	260 / 1,050 (25%)	31 / 112 (28%)	291/1,162 (25%)	0.065
Hypertensive disorder of pregnancy	12%(343/2867)	18%(79/446)	422/3,313 (13%)	0.001	115 / 1,050 (11%)	21 / 112 (19%)	136/1,162 (12%)	0.359
labor induction	39% (1107/2867)	60% (266/446)	1,373/3,313 (41%)	> 0.9	457 / 1,050 (44%)	62 / 112 (55%)	519/1,162 (45%)	0.056
Bishop score before the trial of labor								0.001
bishop ≤ 4	19%(541/2867)	33%(149/446)	690/3,313 (21%)		229 / 1,050 (22%)	33 / 112 (29%)	262/1,162 (23%)
4＜bishop ≤ 6	44%(1260/2867)	43%(191/446)	1,451/3,313 (44%)	0.028	355 / 1,050 (34%)	49 / 112 (44%)	404/1,162 (35%)
bishop ≥ 7	37%(1066/2867)	24%(106/446 )	1,172/3,313 (35%)	0.002	466 / 1,050 (44%)	30 / 112 (27%)	496/1,162 (43%)
Ways of labor induction								0.263
none	61%(1760/2867)	40%(179/446)	1,939/3,313 (59%)		594 / 1,050 (57%)	50 / 112 (45%)	644/1,162 (55%)
One method	24%(678 / 2,867)	30%(134/446)	812/3,313 (25%)	> 0.9	286 / 1,050 (27%)	29 / 112 (26%)	315/1,162 (27%)
Two methods	12%(352/ 2,867)	20%(89/446)	441/3,313 (13%)	> 0.9	140 / 1,050 (13%)	17 / 112 (15%)	157/1,162 (14%)
Three methods or more	2.7%(77 / 2,867)	9.9%(44/446)	121/3,313 (3.7%)	> 0.9	30 / 1,050 (2.9%)	16 / 112 (14%)	46/ 1,162 (4.0%)
estimation of fetal weight(EFW) measured by ultrasongraphy within one week before delivery	3212.88 ± 346.75	3461.62 ± 375.26	3,242.12 (359.18)	< 0.001	3,220.00 ± 359.10 33.97 (3.85)	3,424.42 ± 359.26	3,235.33 ± 362.93	0.482
amniotic fluid index (AFI) measured by ultrasongraphy within one week before delivery	12.13 ± 3.42	12.27 ± 3.91	12.16 ± 3.48	0.054	11.89 ± 3.50	12.05 ± 3.50	11.91 ± 3.50	0.016
Leukocyte count within one week before delivery	8.36 ± 2.65	10.00 ± 4.14	8.58 ± 2.95	0.6	8.70 ± 2.32	10.95 ± 4.40	8.92 ± 2.68	< 0.001
neutrophil count within one week before delivery	7.64 ± 2.69	9.55 ± 4.03	7.90 ± 2.98	< 0.001	6.49 ± 2.17	8.90 ± 4.44	6.72 ± 2.58	< 0.001
Delivery at night	50.8%(1458/2867)	56.6%(252/446)	1,702/3,313 (51%)	＞0.9	501 / 1,039 (48%)	69 / 109 (63%)	576 / 1,162 (50%)	0.290
Analgesia	16%(472/2867 )	24% (107/446)	579/3,313 (17%)	0.7	208 / 1,050 (20%)	31 / 112 (28%)	239 / 1,162 (21%)	0.019
Latent period duration	6.41 ± 4.01	11.28 ± 5.00(N = 159)	6.66 ± 4.21	< 0.001	6.56 ± 4.23	10.29 ± 5.35(N + 35)	6.68 ± 4.32	0.794
Interval of the orifice of uterus opening to 3 cm	4.28 ± 2.84	6.26 ± 3.51(N = 159)	4.38 ± 2.91	< 0.001	4.20 ± 2.90	4.94 ± 3.58(N = 35)༉	4.22 ± 2.93	0.059

Table 2

Multivariable Logistic Regression Model of Selected Factors Associated With Intrapartum Cesarean Section
	Odds Ratio	95% CI		p-value
Primipara
No	Ref
Yes	18.280	10.181	32.823	< 0.001
EFW measured by ultrasongraphy within one week before delivery	1.139	1.100	1.179	< 0.001
Maternal age	1.098	1.063	1.135	< 0.001
Height	0.885	0.864	0.907	< 0.001
Weight at delivery	1.021	1.008	1.034	0.002
Bishop score before the trial of labor
bishop ≤ 4	Ref
4＜bishop ≤ 6	0.721	0.547	0.951	0.021
bishop ≥ 7	0.636	0.441,	0.917	0.015
gestational age	1.038	1.019	1.056	< 0.001
Hypertensive disorder of pregnancy
No	Ref
Yes	1.665	1.215	2.282	0.002
Labor induction
None	Ref
One method	1.270	0.937	1.722	0.123
Two methods	1.335	0.938	1.900	0.109
Three methods or more	2.445	1.480	4.041	< 0.001

Several studies have reported the association between these individual parameters and morbidity, however, data that have quantified the specific risk given individual patient characteristics are limited. In this study, we developed and validated a model to predict the risk of intrapartum cesarean section based on maternal characteristics, laboratory, and image examination as well as pregnancy complications before a trial of labor. The prediction model is useful to guide clinicians to individually evaluate the risks of intrapartum cesarean section and offer a consultation before the labor trial. The risk factors in this prediction model include older maternal age, shorter in height, longer gestational age, heavier in weight, primipara, lower Bishop score, complicated hypertensive disorder, receiving labor induction and heavier estimation of fetal weight(EFW) within one week before delivery.

After China introduced an overall two-child policy, the proportion of pregnant women with advanced age, obesity, gestational diabetes, gestational hypertension disorder, and other diseases increased, which was accompanied by the increased rate of delivery complications and cesarean section^[8]. With the implementation of the New Labor Standard, a prolonged latent period was no longer an indication for cesarean section, hence, puerpera were given more time to have a trial of labor. In clinical practice, for patients with maternal complications as well as those who have one or more risk factors without definite indications of cesarean section, it is necessary to reduce the intervention and give them sufficient trial time based on the New Labor Standard. However, these cases often transform to cesarean section because of arrest active phase, fetal distress, or intrapartum fever, which increases the risk of postpartum hemorrhage and puerperal infection. Thus, there are still various details that need to be explored and improved in clinical practice.

However, there is no risk assessment tool for intrapartum cesarean section at home. Meanwhile, research on attempting to predict the outcome of delivery after labor induction and nulliparous low-risk populations has been reported abroad^[9–12]. Most of these models were built based on Western populations, and few models have been applied in clinical practice. Therefore, we innovatively developed two different models to quantify risk factors and predict the risk of intrapartum cesarean section, which can be used to evaluate the labor process in different time nodes, particularly for those with poor progress in the latent period. However, prolonged latent periods did not increase maternal and fetal complications^[13]. On the contrary, a prolonged latent period to some degree implied a higher risk of dystocia. Longer duration of the uterine contraction and fetal head compression is associated with increased risks for cervical edema, which entail more cervical examinations, and a high rate of intervention places parturients at significantly greater risk of developing an infection, cervical laceration, fetal distress, and postpartum hemorrhage. Hence, the models we developed may be useful for clinicians to estimate before a trial of labor and reassess the risk during the labor. Meanwhile, for those at high risk of cesarean section, a cesarean section may be elected earlier to avoid "excessive" trial labor and reduce the potential excessive risks of intrapartum cesarean delivery. Otherwise, for those low-risk women, vaginal trial labor should be encouraged, standardized the labor process management, which can reduce the cesarean section rate, and reduce the incidence of vaginal delivery complications.

In this multivariate logistic regression model, parity plays the strongest role in the intrapartum cesarean section, which is a coincidence with previous studies^[14]. Compared with the multipara, cervical dilation is relatively slow and the labor process is long in the nulliparous. Tension and fear of parturient can cause an increase in catecholamine secretion, thereby leading to the fluctuation of blood pressure and plasma glucose and eventually to cesarean section because of fetal distress. Maternal age and gestational age may also affect the progress of labor. Therefore, as maternal age increases, the elasticity of tissues decreases and the myometrium becomes less responsive and sensitive to oxytocin, which may extend the labor process and lead to more interventions^[15]. With the gradual maturation of the fetus, the skull bone becomes hard, which leads to poor skull overlap during delivery. In addition, the placental function decreases, which may increase the risk of fetal distress or relative cephalopelvic disproportion^[16].

Additionally, numerous studies on predicting the outcome of labor induction choose the Bishop score as a predictor, taking it as a good index to evaluate cervical ripening^[11,17,18]. However, because of the measurement of the Bishop’s score, some scholars proposed that it was relatively subjective, thereby leaving a problem in light of reproducibility among observers, which was not recommended for risk prediction. Therefore, in some research, the Bishop score was replaced by certain measurements of the cervix by ultrasonography ^[19,20]. Herein, ultrasound elastography was recently reported to evaluate cervical ripening by quantitatively evaluating the softness and hardness of tissue, however, this technology still needs further research to verify its evaluation efficiency^[21]. Ultrasound can accurately measure the length of the cervix, however, it requires suitable training and equipment, which results in limited use to a certain extent. In addition, systematic reviews have no evidence to confirm that ultrasound measurement is superior to Bishop’s score^[22,23]. Apart from that, a Guide of the National Institute for Health and Care Excellence currently continues recommending the appraisal of Bishop’s score before induction^[24]. In our study, we still use the Bishop’s score as a predictor in model A and stratified the Bishop score. As for model B, given that all patients already enter the active phase, we then excluded the Bishop score and included the latency duration to optimize the model. These two models show good prediction efficiency and can be used in all circumstances.

Moreover, excessive fetal weight leads to intrapartum cesarean section and was used as a predictor in a prior model. However, neonatal birth weight is unknown before the delivery. In this univariate analysis, we found that the estimation of fetal weight measured by ultrasound within one week before the delivery was statistically significant between the vaginal delivery group and the intrapartum cesarean section group. Therefore, we use ultrasound-obtained estimated fetal weight as predictors, thus hoping to reduce postpartum hemorrhage, cervical laceration, and other delivery complications caused by excessive trial labor through this parameter.

Furthermore, most of the patients delivered to our center had regular prenatal examinations, underwent a diagnostic test for gestational diabetes, and had regular blood pressure monitoring. Thus, the rate of missed diagnosis of gestational diabetes and gestational hypertension decreased. Hence, the proportion of gestational diabetes mellitus, hypertensive disorder of pregnancy, and undergoing assisted reproductive technologies is relatively high in our center, and most of them could endure vaginal delivery. Therefore, based on this fact, we studied the relationship between gestational diabetes mellitus, hypertensive disorder of pregnancy, assisted reproductive technologies, and intrapartum cesarean section and eventually include hypertensive disorder of pregnancy and assisted reproductive technologies as predictors in our model.

Despite the high sensitivity and specificity of the multivariate logistic regression model, the calculation process of the model is relatively complex. Consequently, in this study, we converted it into a nomogram, in which all parameters are quantized. Thus, the predicted probability of intrapartum cesarean section can be directly obtained by adding the scores, which have better clinical practicality.

This study uses a large sample size after the new production process standard to build the prediction model. The model, not limited to those low-risk nulliparous, could cover general cases. Furthermore, it is the first time to incorporate the latent period duration as a predictor. Herein, we built two models to evaluate the risk of intrapartum cesarean section at different times of the labor process, which can dynamically assess individual risk and provide a precise prospect of the trial of labor. The predictors include maternal characteristics, fetal biometry, and laboratory examination, all of these parameters are easy to access. However, our study is limited by the availability of single center data and retrospective study and there was a certain degree of bias in the study because of the quality of medical records.

Herein, the prediction models displayed by nomograms are supposed to be clinically handy and simple tools for predicting intrapartum cesarean section. These nomograms can provide an individualized assessment and consultation under the New Labor Standard by using easily identifiable demographic and biometric data, particularly for those without specific maternal or fetal indications but requiring cesarean section. For women at a high risk of intrapartum cesarean section, the potential risks involved in pursuing a vaginal delivery versus an elective cesarean section should be provided to allow them to make informed choices before the trial of labor. Moreover, during the labor process, this risk assessment tool can help clinicians better monitor the labor process and decide whether or not to continue the trial labor or transfer to the cesarean section in time. Meanwhile, as for the low-risk population, this model reassures most women regarding their likely success at having an uncomplicated vaginal delivery and encourages them to pursue vaginal labor.

Funding

This work was supported by the Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities (Grant numbers PKU2022LCXQ034)

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

YY Liu: protocol/project development, data collection and management, data analysis, manuscript writing.SL Ye: protocol/project development. Y Ma: Data collection. XQ Zhao: Data management. YQ Wang: protocol/project development, editing.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. This study was approved by the Ethics Committee of Peking University Third Hospital (ethical number:2021-173-01)

Acknowledgments

We are grateful for the support from the Information Management and Big Data Center of the Peking University Third Hospital. Our warmest thanks go to all the clinicians working in the Department of Obstetrics and Gynaecology of Peking University Third Hospital for their contribution in collecting data during the study and all women who participated in the study.

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Construction and verification of prediction models for intrapartum cesarean section to be used at full-term pregnant women with singleton and cephalic presentation under the new labor standard: a retrospective case-control study

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