Ooba et al. BMC Pregnancy and Childbirth(2024) 24:542
https://doi.org/10.1186/s12884-024-06744-0

BMC Pregnancy and ChildbirthOpen Access

RESEARCH

Evaluating the impact of a trial of labor
after cesarean section on labor duration:a retrospective cohort study
Hikaru Ooba1, Jota Maki1* and Hisashi Masuyama1
Abstract
Background Cesarean section (C-section) rates are increasing globally, and repeated C-sections are associatedwith increased maternal morbidity. Trial of labor after C-section (TOLAC) is an approach to reduce the recurrence of
C-sections. However, limited research exists on the impact of cesarean scars on labor duration in TOLAC, considering
the termination of labor through C-section and selection bias. This study aimed to investigate the impact of cesareanscars on labor duration in TOLAC participants, accounting for potential confounding factors and biases.
Methods This retrospective cohort study included 2,964 women who attempted vaginal birth at a single center inJapan from 2012 to 2021. The study categorized participants into TOLAC (n = 187) and non-TOLAC (n = 2,777) groups.
Propensity scores were calculated based on 14 factors that could influence labor duration, and inverse probabilityof treatment weighting (IPTW) was applied. Cox proportional hazards regression analysis estimated hazard ratios(HRs) for labor duration, with and without IPTW adjustment. Sensitivity analyses used propensity score matching,bootstrapping, and interval censoring to address potential biases, including recall bias in the reported onset of labor.
Results The unadjusted HR for labor duration in the TOLAC group compared to the non-TOLAC group was 0.83
(95% CI: 0.70–0.98, P = 0.027), indicating a longer labor duration in the TOLAC group. After adjusting for confounding
factors using IPTW, the HR was 0.98 (95% CI: 0.74–1.30, P = 0.91), suggesting no significant difference in labor duration
between the groups. Sensitivity analyses using propensity score matching, bootstrapping, and interval censoringyielded consistent results. These findings suggested that the apparent association between TOLAC and longer labor
duration was because of confounding factors rather than TOLAC itself.
Conclusions After adjusting for confounding factors and addressing potential biases, cesarean scars had a limited
impact on labor duration in TOLAC participants. Maternal and fetal characteristics may have a more substantial
influence on labor duration.
Keywords Labor duration, Trial of labor after cesarean section, Vaginal birth, Cesarean section, Propensity scores,
IPTW*Correspondence:
Jota Maki
jotamaki@okayama-u.ac.jp
1
Department of Obstetrics and Gynecology, Okayama University Hospital,
2-5-1 Shikata-cho, Kita-Ku, Okayama City 700-8558, Okayama Prefecture,Japan© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Ooba et al. BMC Pregnancy and Childbirth(2024) 24:542

Introduction
The prevalence of cesarean section (C-section) is on therise globally [1], with some nations reporting that up to
50% of births are conducted via C-Sect. [2]. In Japan,
the rate has increased to over 18% [3], and tertiary carefacilities indicate that 37.3% of childbirths are C-Sect. [4].
Such repeated procedures are associated with extended
surgical durations, increased risk of severe adhesions,higher blood loss, and elevated transfusion needs [5].
Furthermore, post-operation women may face complications such as infertility, high-risk subsequent pregnancies, postpartum menorrhagia, and dysmenorrhea [6].
These symptoms threaten their quality of life.
Trial of labor after C-section (TOLAC) offers a promising approach to reduce the frequency of repeat cesarean births and their adverse effects [7]. Successful vaginal
birth after C-section (VBAC) [8] not only presents lower
infection, fever, and postpartum hemorrhage risks but
also proves to be more cost-efficient than elective repeat
cesarean delivery (ERCD) [9]. While successful TOLAC isassociated with the least maternal morbidity, the hazards
of failed TOLAC surpass those of planned repeat C-Sect.
[10]. Factors such as occiput-posterior fetal position,
extended second labor stage, maternal age, and largefor-gestational-age fetuses contribute to TOLAC failure[11]. The primary concern with unsuccessful TOLAC isuterine rupture, which prompts many hospitals to proceed cautiously with TOLAC attempts [12]. Additionally,
the duration of labor, especially the length of the second
stage, is a critical risk factor for uterine rupture during
TOLAC [13].
Research on labor duration in women with previous
C-sections remains limited. A study focusing on cervical dilation time during the first labor stage [7] reported
median durations of 3.0 h for TOLAC and 2.8 h for nonTOLAC participants. However, factors such as maternal
age and parity influence labor duration, rendering simple
time comparisons potentially biased. Previous investigations of TOLAC labor duration have often overlooked
cesarean terminations and failed to adjust for variables
affecting labor time. Our study aimed to reveal theimpact of cesarean scars on the duration of vaginal laborwith these limitations.
Methods
Study design

This retrospective cohort study utilized data from a single medical center from January 1, 2012, to December 31,
2021. In the institution, women with a prior C-section
were provided with written information about the risks
and benefits of TOLAC and ERCD during antenatal visits
before the onset of labor. Informed consent was obtained
regarding the chosen mode of delivery. Women who opt
for TOLAC were also informed about the possibility ofPage 2 of 9

requiring an emergency C-section during labor, basedon the same criteria as for women without a prior C-section. The decision to attempt TOLAC or undergo ERCD
was made by the women themselves, and those whochoose TOLAC received the same standard of care as
women undergoing a normal vaginal birth. Labor induction was not routinely performed for women undergoing TOLAC unless deemed necessary by the attending
obstetrician. We included singleton pregnancies between
37 weeks 0 days and 41 weeks 6 days, both spontaneousand induced labor. The exclusion criteria were elective
C-sections, preterm births before 37 weeks, post-term
births after 42 weeks, intrauterine fetal demise (IUFD),
and multiple gestations. Comprehensive information onmaternal background, medical history, delivery details,
and postnatal and neonatal care was obtained from electronic health records. Data were extracted according topredefined common categories provided by the Japan
Society of Obstetrics and Gynecology Perinatal Database. Participants who underwent vaginal birth werecategorized into TOLAC and non-TOLAC groups basedon their history of C-section. Participants who required
emergency C-section during the vaginal birth trial were
censored. Labor duration was defined as the total timefrom labor onset to delivery, encompassing both the firstand second stages of labor. The onset of labor was determined based on the participants’ self-reports.
Statistical analysis

We assumed an effect size of 0.20, alpha error of 0.05,
beta error of 0.20, and dropout rate of 5%. Based on the2021 birth statistics of the facility, we presumed the proportion of TOLAC to be 0.07, resulting in a calculated
sample size of 3,007 participants. Influenced by priorstudies [9, 14–18], we identified 14 factors potentiallyaffecting labor duration. These factors included maternal age, Body Mass Index (BMI), maternal nationality,
history of vaginal birth, pre-pregnancy smoking habits,
gestational diabetes, premature rupture of membranes,
fetal sex, birth weight, fetal position, induction intended
to promote labor, labor analgesia, uterine fundal pressure, and vacuum-assisted delivery. These factors wereused to calculate propensity scores. The distributions ofthese variables are presented in Table 1. For each factor, continuous variables were analyzed using the t-test,
while categorical variables were examined using the chisquare test. The Standardized Mean Difference (SMD)[19] was also calculated. Data with missing information
on delivery time or with more than 25% missing values
for any item were excluded. Given that the database wasregularly updated by medical staff immediately afterchildbirth, missing information was assumed to be missing at random (MAR) [20]. Multiple imputations were
employed to address the missing values. Subsequently,

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Table 1 Participant background
Characteristic

N

Maternal age
Mean (SD)Maternal body mass index
Mean (SD)
Unknown
Maternal country of origin
Japanese
Other country
History of vaginal birth(-)(+)
Smoking(-)(+)
Unknown
Gestational diabetes mellitus(-)(+)
Premature rupture of membranes(-)(+)
Fetal sex
FemaleMale
Fetal birth weight
Mean (SD)
Fetal position
Cephalic position
breech presentation
Unknown
Labor induction(-)(+)
Unknown
Labor analgesia(-)(+)
Vacuum-assisted delivery(-)(+)
Unknown
Uterine fundal pressure(-)(+)
Unknown
Censor
(-)
(+)

2,964

Non-TOLAC
N = 2,7771

TOLAC
N = 1871

31 (5)

33 (5)

25.4 (3.6)
46

25.9 (3.4)
42,626 (95%)
151 (5.4%)

172 (92%)
15 (8.0%)

1,490 (54%)
1,287 (46%)

146 (78%)
41 (22%)

1,611 (84%)
296 (16%)870

108 (83%)
22 (17%)57

2,586 (93%)
191 (6.9%)

161 (86%)
26 (14%)

1,895 (68%)
882 (32%)131 (70%)
56 (30%)1,364 (49%)
1,413 (51%)

88 (47%)99 (53%)

3,097 (382)

3,091 (440)

2,730 (98%)
44 (1.6%)
3

186 (99%)
1 (0.5%)02,260 (82%)
509 (18%)8181 (97%)
6 (3.2%)0

2,582 (93%)
195 (7.0%)

172 (92%)
15 (8.0%)

2,441 (91%)
229 (8.6%)
107

118 (84%)
23 (16%)46

2,570 (93%)
206 (7.4%)
1

187 (100%)0 (0%)
02,670 (96%)
107 (3.9%)

141 (75%)
46 (25%)

2,914

2,964

2,964

2,037

2,964

2,964

2,964

2,964
2,961

2,956

2,964

2,811

2,963

2,964

SMD2

95% CI23

p-value4-0.41-0.56, -0.26< 0.001-0.13-0.28, 0.02

0.077

0.10

-0.04, 0.25

0.2

0.53

0.38, 0.68

< 0.001

0.04-0.14, 0.22

0.8

0.23

0.08, 0.38

< 0.001

0.04-0.11, 0.19

0.7

0.04

-0.11, 0.19

0.6

0.01

-0.13, 0.16

0.9

0.10

-0.05, 0.25

0.4

0.50

0.36, 0.65

< 0.001

0.04-0.11, 0.19

0.7

0.24

0.07, 0.41

0.003

0.40

0.25, 0.55

< 0.001

0.62

0.47, 0.77

< 0.001

Ooba et al. BMC Pregnancy and Childbirth(2024) 24:542

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Table 1 (continued)
Characteristic

N

Duration of delivery
Mean (SD)2,964

Non-TOLAC
N = 2,7771

TOLAC
N = 1871

614 (563)

576 (597)

SMD2

95% CI23

p-value4

0.07-0.08, 0.21

0.4

1

n (%)

2

Standardized Mean Difference3

CI = Confidence Interval

4Welch Two Sample t-test; Pearson’s Chi-squared test

logistic regression was used to calculate propensity
scores based on the 14 factors above. The area under the
Receiver Operating Characteristic curve (ROC-AUC) of
the propensity scores was computed. For the TOLAC
group, weights were determined as the inverse of the propensity score, while for the control group, weights were
the inverse of one minus the propensity score, calculating
the inverse probability of treatment weighting (IPTW)[21]. These weights were then applied to the dataset. To
address the increased variance in estimates due to propensity scores being close to zero or one, we trimmed
the top and bottom 1% of the propensity scores. Survival
curves for each labor duration were created, from whichlabor duration curves were depicted, and hazard ratios
were estimated using Cox proportional hazards regression analysis. Statistical analyses were conducted using R
software (version 4.2.3, R Foundation for Statistical Computing, Vienna, Austria).
Outcome

The primary outcome was designated as the hazard
ratio for labor duration based on the presence of cesarean scars following the application of IPTW. The secondary outcome was determined as the hazard ratio forlabor duration associated with cesarean scars without theapplication of IPTW.
Sensitivity analysis

IPTW estimates the average treatment effect (ATE)across the entire trial population, including participants
with and without cesarean scars. However, extreme
propensity scores can lead to unstable estimates [22].
Therefore, as a sensitivity analysis, we conducted anassessment using propensity score matching. By matching participants with similar propensity scores fromboth the exposed and control groups, the distribution ofcovariates in the matched subset became closer to that in
the exposed study population. Propensity score matchingand IPTW have different assumptions and limitations,allowing for the strengthening of result robustness by
examining the effects in both populations.
Considering the potential dependency of the results
on a specific dataset, a sensitivity analysis was conductedusing the bootstrap method. The bootstrap algorithm canbe used to align the values of the explanatory variables
with those of a given target distribution [23]. We randomly resampled the original dataset to generate bootstrap samples. For each sample, Cox proportional hazard
models were applied both with and without IPTW to calculate hazard ratios. The distribution of hazard ratios wasestimated from the obtained samples. This process was
repeated 1,000 times.
To address potential recall bias in the reported onset
times of labor, we performed a sensitivity analysis using
interval censoring [24]. Interval censoring is a methodthat allows for uncertainty in the exact timing of anevent, such as the onset of labor, by using an interval
within which the event is known to have occurred. Using
interval censoring, we aimed to assess the robustnessof our findings to potential inaccuracies in the reported
onset times of labor. The lower bound of the interval
was defined as the reported onset time of contractions
minus a specified duration, while the upper bound wasset as the reported onset time plus the specified duration.
We considered three different durations: 4, 8, and 12 h,
to account for varying degrees of potential recall bias. In
cases where the lower bound of the interval was negative,
it was rounded to 0.1 to ensure non-negative survival
times. Using the obtained interval-censored data, weestimated the HRs and their 95% CIs for the association
between TOLAC and the duration of labor. The analyses
were conducted both with and without IPTW to accountfor potential confounding factors.

Results
During the observation period, 3,707 women gave birth.
A total of 723 women were excluded due to elective
C-section (n = 582), multiple gestations (n = 91), preterm
births before 37 weeks (n = 141), post-term births after 42
weeks (n = 4), and IUFD (n = 73). Of the remaining 2,984
women who attempted vaginal birth, 20 were excluded
due to incomplete data on labor duration, resulting in
2,964 participants being included in the analysis. The
non-TOLAC group consisted of 2,777 women (93.7%),
whereas the TOLAC group included 187 women (6.3%).
During the observation period, 46 individuals (25.4%)
in the TOLAC group and 107 individuals (3.9%) in the
non-TOLAC group were censored because of emergency

Ooba et al. BMC Pregnancy and Childbirth(2024) 24:542

C-sections. Figure 1 illustrates the recruitment process. Overall, compared to the non-TOLAC group, the
TOLAC group included women of older age, and had
a higher prevalence of gestational diabetes, vacuumassisted delivery, and emergency C-section. Women witha history of vaginal birth, labor induction, and uterine
fundal pressure were more common in the non-TOLAC
group. The variable for maternal smoking was excluded
due to more than 25% missing data. The characteristics ofthe study population are presented in Table 1. The ROCAUC of the propensity score was 0.80 (95% CI: 0.77–0.84). Figure 2 summarizes the probability density of thepropensity scores for TOLAC and non-TOLAC women.
As expected, the distribution of propensity scores for the
TOLAC group shifted towards 1, while that for the nonTOLAC group shifted towards 0.
Figure 3 displays the labor duration curves for participants stratified by TOLAC without IPTW, along with the
95% CI. The Log-Rank test yielded a result of P = 0.03.
According to the Cox proportional hazards analysis, theHR for TOLAC was 0.83 (95% CI: 0.70–0.98, P = 0.027),

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indicating a longer labor duration in the TOLAC groupwhen not adjusting for confounding factors. Figure 4
shows the labor duration curves for participants stratified
by TOLAC with IPTW. The Log-Rank test resulted in
P = 0.70. The Cox proportional hazards analysis revealed
an HR for TOLAC of 0.98 (95% CI: 0.74–1.30, P = 0.91),
suggesting no significant difference in labor duration
between the TOLAC and non-TOLAC groups after
adjusting for confounding factors using IPTW.
The results of the sensitivity analyses are presented
in Supplementary Tables 1 and 2. The propensity scorematching analysis yielded an HR of 1.02 (95% CI: 0.81–
1.28, P = 0.88), which was consistent with the main analysis using IPTW (HR 0.98, 95% CI: 0.74–1.30, P = 0.91).
The bootstrap analysis also produced similar results,
with an HR of 0.83 (95% CI: 0.70–0.97) without IPTW
and 1.07 (95% CI: 0.87–1.33) with IPTW. The interval
censoring analysis (Supplementary Table 2) showed thatthe HRs for TOLAC decreased as the interval range
increased when not adjusting for confounding factors(± 4 h: HR 0.68, 95% CI: 0.48–0.97, P=0.031; ±8 h: HR

Fig. 1 Flowchart of the study population. TOLAC: trial of labor after cesarean section; VBAC: vaginal birth after cesarean section

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Fig. 2 Density plot of propensity scores for the trial of labor after cesarean section (TOLAC, green) and non-TOLAC (red) groups

0.45, 95% CI: 0.24–0.83, P = 0.011; ±12 h: HR 0.40, 95%
CI: 0.20–0.79, P = 0.008). However, after adjusting forconfounding factors using IPTW, the HRs were closer
to 1 and not statistically significant (± 4 h: HR 1.00, 95%
CI: 0.90–1.11, P = 0.96; ±8 h: HR 1.05, 95% CI: 0.87–1.27,
P = 0.63; ±12 h: HR 1.23, 95% CI: 0.99–1.51, P = 0.06).
These findings suggest that the observed association
between TOLAC and labor duration is robust to potential recall bias in the reported onset of labor, particularly
after adjusting for confounding factors.

Discussion
When not adjusting for covariates through IPTW, theduration of labor was significantly longer for those undergoing TOLAC than for those who did not (HR = 0.83,
95% CI: 0.70–0.98, P = 0.027). After applying IPTW toaccount for potential confounders, the difference in labor
duration between the two groups was not significant(HR = 0.98, 95% CI: 0.74–1.30, P = 0.91). These findings
suggested that the apparent association between TOLAC
and longer labor duration was because of confounding
factors rather than TOLAC itself. These trends remained
consistent even after conducting sensitivity analyses
using propensity scores and the bootstrap method. It alsosuggested that the observed relationship is robust evenafter accounting for potential recall bias in the reported
start time of delivery.
As emphasized in prior research [25, 26], the mostsignificant predictor of successful TOLAC is a historyof vaginal birth. The likelihood of a successful VBAC
increases if there is a previous history of vaginal birth,previous successful TOLAC, or if natural labor commences during a current pregnancy with attempted
TOLAC [27]. Studies examining the duration of TOLAC
labor stratified based on the history of vaginal birth [28]indicated that TOLAC groups without a history of vaginal birth were comparable to nulliparous control groups.
Conversely, the TOLAC groups with a history of vaginal
birth were similar to the multiparous control groups,
suggesting that the diagnosis of dysfunctional labor
should be based on the number of births. Another study
focusing on the first phase of the labor curve and the rateof cervical dilation temporarily [29] found no significant
differences between women who underwent TOLAC
and those without previous C-sections. This researchconcludes that women undergoing TOLAC should be
diagnosed with labor dysfunction under the same criteria as those without cesarean scars. Study investigating
the labor patterns of primary parous women undergoing TOLAC without a history of vaginal birth and thoseOoba et al. BMC Pregnancy and Childbirth(2024) 24:542

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Fig. 3 Labor duration curves for the trial of labor after cesarean section (TOLAC, green) and non-TOLAC (red) groups without inverse probability of treatment weighting (IPTW). Shaded areas represent 95% confidence intervals

experiencing natural labor [30] reported no differencesin the first stage of labor. However, the median durationof the second stage of labor was slightly longer in the
TOLAC group. The authors considered that they could
not rule out the possibility that a higher incidence ofbasic labor complications contributed to the longer duration of labor observed. In this study, we included “history of vaginal birth” as one of the adjustment factors.
Considering the lower proportion of women with a history of vaginal delivery in the TOLAC group, our result
suggested that the observed impact on labor durationin the TOLAC group before adjustment could be partially attributed to the difference in vaginal birth history
between the two groups.
Our study was novel in its examination of labor duration through the adjustment of participant profiles. However, this study had several limitations. First, the lack ofdata regarding the reasons for the previous C-section in
the TOLAC group might have introduced selection bias.
Nevertheless, we believe that by calculating propensity
scores based on detailed maternal and fetal backgrounds
and procedures, we were able to compare participants
with similar backgrounds between the TOLAC and nonTOLAC groups after adjustment. Secondly, the onset oflabor was based on self-reporting by pregnant women,
and recall bias could not be eliminated. Although sensitivity analyses using interval censoring consistently
showed similar trends, the issue was not entirely resolved.
The reporting of the onset of labor by pregnant women
was randomly erroneous without systematic bias, andthe strength of the association may have been underestimated due to non-differential misclassification, biasing
the results towards the null [31]. Third, this single-center
study targeted a relatively stable population of pregnant
women. The decision to attempt TOLAC is made by the
women themselves after being informed of the risks andbenefits. This may have influenced the outcomes of ourstudy, as women who chose TOLAC might have had different characteristics or motivations compared to those
who opted for ERCD. Fourth, this study utilized datacollected over 10 years from January 1, 2012, to December 31, 2021. During this time, there might have beenchanges in clinical practice, such as advancements inobstetric care, updates to guidelines, or shifts in patient
demographics. These potential changes could influence
the management of TOLAC and the interpretation of the
combined findings. Fifth, it was not possible to separately
evaluate the first and second stages of labor.

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Fig. 4 Labor duration curves for the trial of labor after cesarean section (TOLAC, green) and non-TOLAC (red) groups with inverse probability of treatment
weighting (IPTW). Shaded areas represent 95% confidence intervals

Conclusion
The impact of cesarean scars was limited, and the analysis after adjustment with IPTW suggested that other factors such as maternal physique and fetal characteristics
had a more substantial influence.
Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12884-024-06744-0.
Supplementary Material 1
Supplementary Material 2
Acknowledgements
We acknowledge Dr. Hironori Ito and Dr. Miho Kanemori for theircontributions to data collection.
Author contributions
Hikaru Ooba contributed to concept development, data curation, formal
analysis, investigation, resources, visualization, and manuscript writing. Jota
Maki contributed to formal analysis and methodology, manuscript review
and editing, and final approval. Hisashi Masuyama contributed to researchsupervision and project management.
Funding
This study did not receive any financial support.

Data availability
Availability of data and materials: The datasets generated and/or analyzed
during the current study are available from the corresponding author upon
reasonable request.

Declarations
Ethics approval and consent to participate
Okayama University Graduate School of Medicine, Dentistry andPharmaceutical Science and Okayama University Hospital Ethics Committee
reviewed and approved the research protocol (approval number 2208004), and informed consent was obtained from each participant. This study
complied with the Declaration of Helsinki for human research and provided
written informed consent for the use of data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 28 February 2024 / Accepted: 8 August 2024

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