Developing a nomogram for estimating the risk of needing to perform a caesarean section after induction of labour in pregnancies using a COOK ® Cervical Ripening Balloon

Abstract

BACKGROUND:

Using a COOK^® Cervical Ripening Balloon (CCRB) for cervical maturity has become a common clinical practice for the induction of labour (IOL).

OBJECTIVE:

To develop and validate a predictive instrument that could estimate the risk of a caesarean after IOL in term pregnancies with CCRB treatment.

METHODS:

The medical records of 415 pregnant women requiring IOL from January 2018 to October 2022 were retrospectively reviewed and randomly selected for training (290) and validation (125) sets in a 7:3 ratio. A model for predicting the risk of a caesarean was virtualised by a nomogram using logistic regression analysis.

RESULTS:

After completing the multivariate analysis, parity (odds ratio [OR] $=$ 0.226; $p=$ 0.017), modified Bishop score at induction (OR $=$ 0.688; $p=$ 0.005) and the artificial rupture of membranes (OR $=$ 0.436; $p=$ 0.010) were identified as the predictors for implementing a caesarean delivery after IOL. The decision curve analysis showed that the model achieved a net benefit across all threshold probabilities.

CONCLUSION:

We successfully constructed a nomogram for caesarean delivery after IOL in pregnancies with CCRB treatment using factors including parity, modified Bishop score at induction and the artificial rupture of membrane.

Keywords

Induction of labour caesarean delivery nomogram Bishop score cervical ripening balloon

1. Introduction

Induction of labour (IOL) is one of the most common obstetric practices to achieve successful vaginal delivery by cervical ripening [1]. The health of women and their babies, as well as satisfaction with the delivery experience, can be significantly affected by IOL [2]. In recent years, the incidence of IOL has continued to increase, particularly in developed countries, with more than 25% of pregnant women undergoing IOL [3].

A successful IOL depends on the favourability of the cervix [4]. Current options for IOL include the use of oxytocin, prostaglandins and single or double balloon catheter placement [5]. However, to date, an optimal method for IOL has not yet been determined. A COOK^® Cervical Ripening Balloon (CCRB) is currently the most used cervical ripening double-balloon device. Different from a uterine balloon, a CCRB has another vaginal balloon that helps to fix the uterine balloon on the lower uterine segment. It has been reported that the success rate of vaginal delivery after CRB with a CCRB is 66%–70% [6]. The combined use of double-balloon catheters and oxytocin can also induce effective uterine contractions, increasing the success rate of vaginal delivery [7]. For example, Wu et al. [8] demonstrated that combining a double-balloon catheter and oxytocin for IOL had a much higher vaginal delivery rate and shorter total duration of labour compared with using oxytocin alone. Thus, using a CCRB for cervical maturity has become a common clinical practice for IOL.

One of the challenges linked to IOL is that doing so increases the risk of having to perform a caesarean delivery [9]. Purportedly, the risk of complications for the mother and foetus associated with intrapartum caesarean delivery was higher than those associated with antepartum caesarean delivery [10]. Thus, predicting the accuracy rate of having to perform a caesarean delivery after IOL may prevent additional complications for those most at risk. The accurate prediction of needing to perform a caesarean section after IOL will be helpful in clinical practice.

Several prediction models for induction success have been developed [9]. For example, Zhou et al. [9] established a nomogram to estimate the risk of conducting a caesarean delivery before IOL based on factors such as maternal height, age, uterine height, abdominal circumference and estimated foetal weight. Levine et al. [11] demonstrated in their model that factors including nulliparity, gestation age, body mass index (BMI) at delivery, modified Bishop score and height were significantly associated with the caesarean rate after IOL. However, the above prediction models did not include pregnancies using a CCRB for cervical maturity. The common methods for cervical ripening used in these studies were Foley catheters or vaginal misoprostol [9, 12].

The use of a CCRB to trigger cervical ripening, combined with the use of oxytocin, has been widely used for IOL in recent years. Thus, we conducted this study to develop and validate a predictive instrument that could estimate the risk of having to perform a caesarean section after IOL in term pregnancies with CCRB treatment.

2. Materials and methods

2.1 Patients

This retrospective study was approved by the Ethics Committee of The Fourth Hospital of Shijiazhuang in accordance with the Declaration of Helsinki. Due to the nature of a retrospective study, the need for informed consent was waived. All patient records were anonymised and de-identified prior to analysis.

Pregnant women requiring IOL in our hospital from January 2018 to October 2022 were reviewed. The inclusion criteria were as follows: (1) full-term delivery; (2) aged 18 years or older; (3) cephalic presentation; (4) singleton gestation; (5) Bishop score before IOL $<$ 7; (6) pregnant women treated with a CCRB. The main exclusion criteria included: (1) a history of uterine incision; (2) foetal malformation or intrauterine foetal death; (3) the presence of contraindications to vaginal delivery; (4) incomplete data.

The enrolled participants were randomly selected for training and validation sets, and the split ratio was 7:3. The training set was used to train the prediction model. The validation data were used to validate the model.

2.2 Data collection

The medical records of enrolled participants were retrospectively reviewed. These data were double-checked by two obstetricians. Factors enrolled for analysis included maternal age, maternal height, maternal weight, BMI at delivery (defined as the BMI at the time of delivery or at the most recent prenatal visit), gestational age at the time of induction, parity, gravidity, the presence of gestational diabetes mellitus, the presence of gestational hypertension, Induced labor time, total labor process, modified Bishop score at the start of induction and effecting the artificial rupture of membranes (amniotomy).

Neonatal sex and post-natal weight were also related to delivery outcome, but since they are not always routinely established prior to the start of induction, they were not included in our analysis. Dilatation, effacement and station of the presenting parts were the main components of the modified Bishop scores. Thus, we did not analyse these factors independently.

Participants who had a successful vaginal delivery after IOL were classified as ‘successful induction’; those who subsequently had to undergo a caesarean delivery for any reason after IOL were classified as ‘failed induction’.

2.3 Statistical methods

The data analysis was performed using R4.0.3 software. Continuous data were expressed as mean $\pm$ SD and compared by student’s $t$ -test. The qualitative data were expressed as percentages and compared by chi-square or Fisher’s exact tests. A model for predicting the risk of having to perform a caesarean section after IOL was virtualised by a nomogram based on the independent variables extracted from multivariable logistic regression using the training set. The validation set was used to confirm the newly established nomogram. The concordance index (c-index) was used to evaluate the predictive accuracy of the model. Sensitivity and specificity were evaluated by the receiver operating characteristics curve (ROC). Furthermore, the clinical value of the model was evaluated by decision curve analysis (DCA). The Delong test was used to compare the AUC of the ROC curves. A $p$ -value $<$ 0.05 was considered statistically significant.

3. Results

From January 2018 to October 2022, 435 full-term pregnant women who received a CCRB for cervical ripening were analysed in this study. Due to incomplete data, 20 pregnant women were excluded. Therefore, 415 pregnancies with a mean age of 30.40 $\pm$ 3.74 years old were finally enrolled. The primary outcome was having to perform a caesarean section, which was completed in 89 of the 415 inductions (21.45%). Among the enrolled 415 participants, 290 were assigned to the training set, while 125 were assigned to the validation set. The comparative clinical characteristics among the training and validation sets are shown in Table 1. The caesarean delivery rates between the training and validation sets were not significantly different ( $p=$ 0.795). Table 2 shows the comparison of induced labor time and total labor process time between two groups of parturients. The induced labor time in the training group was 1955.91 $\pm$ 836.56 minutes, and the total labor process time was 432.18 $\pm$ 314.10 minutes; The induction time of the validation group was 1912.15 $\pm$ 787.36, and the total duration of labor was 415.27 $\pm$ 300.03 minutes, $P>$ 0.05. The difference in data between the two groups was not statistically significant.

Table 1
The demographic data for the enrolled participants ( $n=$ 415)

Characteristics	Training set ( $n=$ 290)		Validation set ( $n=$ 125)		$p$ value
Maternal age, years, $n$ (%)					0.728
$<$ 25	20	(6.90)	8	(6.40)
25–29	129	(44.48)	58	(46.40)
30–34	119	(41.03)	46	(36.80)
$\geqslant$ 35	22	(7.59)	13	(10.40)
Gravidity, frequency, $n$ (%)					0.156
1	195	(67.24)	73	(58.40)
2–3	88	(30.34)	46	(36.80)
$>$ 3	7	(2.41)	6	(4.80)
Parity, $n$ (%)					0.758
Nulliparous	248	(85.52)	109	(87.20)
Multiparous	42	(14.48)	16	(12.80)
Gestational age at delivery, $n$ (%)					0.637
38 weeks–38 weeks 6 days	44	(15.17)	21	(16.80)
39 weeks–39 weeks 6 days	50	(17.24)	23	(18.40)
40 weeks–40 weeks 6 days	113	(38.97)	53	(42.40)
$\geqslant$ 41 weeks	83	(28.62)	28	(22.40)
BMI at delivery, kg/m², $n$ (%)					0.338
$<$ 25	40	(13.79)	26	(20.80)
25–29.9	145	(50.00)	60	(48.00)
30–34.9	86	(29.66)	32	(25.60)
$\geqslant$ 35	19	(6.55)	7	(5.60)
Gestational diabetes mellitus, $n$ (%)	88	(30.34)	38	(30.40)	0.991
Gestational hypertension, $n$ (%)	50	(17.24)	21	(16.80)	0.913
Modified bishop score at induction, mean $\pm$ SD	3.38 $\pm$ 1.09		3.36 $\pm$ 0.95		0.864
Artificial rupture of membrane, $n$ (%)	123	(42.41)	50	(40.00)	0.666
Cesarean delivery, $n$ (%)	61	(21.03)	28	(22.40)	0.795

Note: BMI, body mass index; SD, standard deviation.

Table 2

Comparison of induced labor time and total labor process between two groups of postpartum women ( $n=$ 415)

Group	Number of cases	Induced labor time (min)	Total stage of labor (min)
Training set	290	1955.91 $\pm$ 836.56	432.18 $\pm$ 314.10
Validation set	125	1912.15 $\pm$ 787.36	415.27 $\pm$ 300.03
$t$	–	0.51	0.63
$P$	–	$>$ 0.05	$>$ 0.05

The univariate analysis using the training set showed that parity ( $p=$ 0.026), modified Bishop score at induction ( $p=$ 0.018) and the artificial rupture of membranes ( $p=$ 0.023) were associated with the risk of having to perform a caesarean delivery after IOL. These 3 clinical candidate predictors were selected for the multivariate analysis. After the multivariate analysis was completed, parity (multiparous vs nulliparous, odds ratio [OR] $=$ 0.226; 95% confidence interval [CI] $=$ 0.066–0.767; $p=$ 0.017), modified Bishop score at induction (OR $=$ 0.688; 95% CI $=$ 0.528–0.896; $p=$ 0.005) and the artificial rupture of membranes (OR $=$ 0.436; 95% CI $=$ 0.233–0.817; $p=$ 0.010) were all identified as predictors for having to perform a caesarean delivery after IOL. The results of the univariate and multivariate analyses are listed in Table 3. Then, a model that incorporated the above independent predictors was developed and presented as a nomogram (Fig. 1).

Table 3

Univariate and multivariate analysis of predictors for Cesarean delivery in the training cohort

Characteristics	Univariate analysis		Multivariate analysis
	OR (95% CI)	$p$ value	OR (95% CI)	$p$ value
Maternal age, years
$<$ 25	Reference
25–29	0.918 (0.247, 3.408)	0.899	–	–
30–34	0.594 (0.221, 1.599)	0.303	–	–
$\geqslant$ 35	0.433 (0.156, 1.198)	0.107	–	–
Gravidity, frequency
1	Reference
2–3	1.697 (0.199, 14.483)	0.629	–	–
$>$ 3	1.437 (0.162, 12.736)	0.745	–	–
Parity
Nulliparous	Reference		Reference
Multiparous	0.252 (0.075, 0.846)	0.026	0.226 (0.066, 0.767)	0.017
Gestational age at delivery
38 weeks–38 weeks 6 days	Reference
39 weeks–39 weeks 6 days	1.321 (0.582, 2.999)	0.506	–	–
40 weeks–40 weeks 6 days	0.513 (0.200, 1.318)	0.166	–	–
$\geqslant$ 41 weeks	0.719 (0.360, 1.435)	0.350	–	–
BMI at delivery, kg/m²
$<$ 25	Reference
25–29.9	0.594 (0.161, 2.194)	0.435	–	–
30–34.9	0.583 (0.193, 1.767)	0.341	–	–
$\geqslant$ 35	1.084 (0.352, 3.337)	0.888	–	–
Gestational diabetes mellitus
No	Reference
Yes	1.049 (0.569, 1.933)	0.878	–	–
Gestational hypertension
No	Reference
Yes	1.597 (0.797, 3.199)	0.187	–	–
Modified bishop score at induction	0.731 (0.564, 0.947)	0.018	0.688 (0.528, 0.896)	0.005
Artificial rupture of membrane
No	Reference
Yes	0.494 (0.269, 0.908)	0.023	0.436 (0.233, 0.817)	0.010

Note: BMI, body mass index; OR, odds ratio; CI, confidence interval.

The C-index for the prediction nomogram was 0.689 (95% CI, 0.622–0.756) in the training set (Fig. 2A) and 0.763 (95% CI, 0.638–0.887, Fig. 2B) in the validation set. The Hosmer-Lemeshow test found no statistical significance in the validation set ( $p=$ 0.158), indicating that no departure from a perfect fit was found. The calibration curve demonstrated good agreement between prediction and observation in the validation set (Fig. 3). To evaluate the clinical usefulness of the model, we conducted a DCA in the validation set. The DCA curve showed that the model achieved a net benefit across all threshold probabilities (Fig. 4).

Subsequently, we compared the diagnostic performance of our prediction model and the modified Bishop score in the validation set. The results showed that the AUC of our prediction model for predicting the need for caesarean delivery was much higher compared with the modified Bishop score (0.763 vs 0.597, $p=$ 0.025; see Fig. 5). These results indicated that our prediction model had good diagnostic value in terms of predicting the need for performing a caesarean delivery following IOL.

4. Discussion

Previously, caesarean risk prediction nomograms had been developed and validated for singleton term pregnancies [10, 13]. However, these nomograms did not include pregnancies using a CCRB for cervical maturity. In the present study, an effective nomogram that used three factors (parity, modified Bishop score at induction and the artificial rupture of membranes) to predict the risk of having to perform a caesarean delivery after IOL in pregnancies using a CCRB was developed and validated. Our results identified that our nomogram was a highly predictive tool that could be applied directly.

In the present study, the artificial rupture of membranes, parity and modified Bishop score at induction were predictors of successful labour induction. The artificial rupture of membranes, or amniotomy, is often used during IOL to stimulate uterine contractions [14]. Benefits of an amniotomy include shorter labour and a decreased risk of maternal morbidity. When performed after cervical ripening, amniotomy can release accumulated endogenous prostaglandins in the amniotic membrane forebag, thereby markedly heightening contractility and labour progression [14]. De Vivo et al. [15] conducted a meta-analysis of randomised controlled trials and found that routine early artificial rupture of membranes after cervical ripening could indeed reduce the interval from induction to delivery without increasing the risk of requiring a caesarean delivery. Battarbee et al. [16] reported that caesarean delivery was less frequently needed among women with an amniotomy compared with women without this procedure. Dick et al. [5] demonstrated that the artificial rupture of membranes as a single mode of IOL could reduce the caesarean delivery rate in women who had previously undergone a caesarean section. Consistent with the evidence presented here, our results showed that the artificial rupture of membranes could significantly affect induction success. Pregnancies that include the artificial rupture of membranes had a much lower risk of requiring a caesarean delivery than those without the artificial rupture of membranes.

Traditionally, high Bishop scores were associated with higher vaginal birth success rates following IOL [17]. In Japan, Hosoya et al. [18] found that a high Bishop score was an independent predictor of vaginal delivery following IOL in nulliparous women. In China, Huang et al. [19] constructed a predictive model for pregnant women with hypertensive disorders who used cervical double balloons in IOL. They found that high cervical Bishop scores before IOL were significantly associated with successful induction [19]. Similarly, Vince et al. [20] reported in their study that the Bishop score was a significant predictor for both successful IOL and vaginal delivery among induced women with an unfavourable cervix. Melkie et al. [21] conducted a meta-analysis and found that pregnancies with low Bishop scores were 4.54 times more likely to experience a failed induction compared with controls. As expected, the Bishop score at induction was identified as a predictor for requiring a caesarean delivery after IOL in our population. According to relevant reports, the effective rate of using the mechanical method of a COOK cervical dilatation balloon to promote cervical ripening is as high as 90%, and the rate of natural childbirth is close to 85%. The feasibility and safety of its application in promoting cervical ripening before IOL in term pregnancies are due to the treatment of prostate preparations, and the incidence and adverse reactions are significantly reduced. When the Bishop score is high, compared with pharmacological methods (prostaglandin or oxytocin), one of the main indications of mechanical methods is poor cervix [22]. Our results indicated that pregnancies with lower Bishop scores had a higher risk of needing a caesarean delivery.

Parity is another predictor we identified in our nomogram. It was reported that nulliparous women had a higher caesarean section rate compared with multiparous women [13]. Levine et al. [11] carried out a comparative investigation involving 815 nulliparous and multiparous pregnancies. In their study, parity combined with other factors including BMI, modified Bishop score and height were found to be predictors for performing a caesarean delivery [11]. In Iran, Hemmatzadeh et al. [13] developed a nomogram for both nulliparous and multiparous pregnancies with an unfavourable cervix. They reported that parity and caesarean section risk assessment were predictors of successful labour induction [13]. Different from the above studies, we included nulliparous and multiparous pregnancies with CCRB treatment in the present study. In our study population, we also found that parity could affect the caesarean risk following IOL as indicated in other studies [11, 13].

Nomograms are statistical predictive models that incorporate independent prediction factors to estimate a prognosis for individual patients [23]. After the nomogram was constructed, we validated the prediction model using the validation set. The AUC of the model was 0.763 in the validation set. Additionally, calibration curve analysis and a Hosmer-Lemeshow test further showed good calibration of our model in the validation set. Finally, we evaluated the clinical usefulness of the model by DCA and found that the model achieved a net benefit across all threshold probabilities, indicating that the nomogram had high clinical usefulness in our studied population. Furthermore, we also found that the AUC of our prediction model for predicting the risk of performing a caesarean section was much higher compared with the Bishop score, indicating that our prediction model had good diagnostic value for predicting a caesarean delivery following IOL.

The present study has several limitations. First, the nomogram was established based on data obtained from a single centre in China. This may have caused selection bias. Second, all the data in the present study were collected retrospectively and the sample size of the study was small. Finally, the nomogram we developed was not validated by an external dataset. Further prospective studies are needed to further validate our model.

5. Conclusion

This study creatively developed a pregnancy prediction model for measuring cervical maturity using CCRB, filling the gaps in this field. We successfully constructed a nomogram for predicting the need to perform a caesarean delivery after IOL in pregnancies with CCRB treatment. The artificial rupture of membranes, parity and modified Bishop score at induction were identified as predictors and incorporated into the nomogram. By providing information about the risk of needing a caesarean delivery following IOL, our nomogram can help to provide pre-induction counselling for pregnancies to improve delivery outcomes.

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of The Fourth Hospital of Shijiazhuang.

Availability of data and materials

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Funding

This study did not receive funding in any form.

Author contributions

XH conceived the study; GJ and ZC participated in the design and data analysis and statistics; and GJ helped draft the manuscript. All authors read and approved the final version of the manuscript.

Footnotes

Acknowledgments

None to report.

Conflict of interest

None of the authors have any personal, financial, commercial, or academic conflicts of interest to report.

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Developing a nomogram for estimating the risk of needing to perform a caesarean section after induction of labour in pregnancies using a COOK ® Cervical Ripening Balloon

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Patients

2.2 Data collection

2.3 Statistical methods

3. Results

Table 1 The demographic data for the enrolled participants ( n = 415)

5. Conclusion

Ethics statement

Availability of data and materials

Funding

Author contributions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
The demographic data for the enrolled participants ( $n=$ 415)