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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 3  |  Issue : 4  |  Page : 230-234

Postpartum hemorrhage following cesarean delivery in women with a scarred uterus: A retrospective cohort study


1 Department of Obstetrics and Gynaecology, Shengjing Hospital of China Medical University; Key Laboratory of Maternal-Fetal Medicine, China Medical University; Research Center of China Medical University Birth Cohort, Shenyang 110004, China
2 Research Center of China Medical University Birth Cohort, Shenyang 110004, China

Date of Submission18-Sep-2019
Date of Web Publication2-Jan-2020

Correspondence Address:
Chong Qiao
Department of Obstetrics and Gynaecology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang 110004
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2096-2924.274548

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  Abstract 


Objective: To develop a model to predict the risk of postpartum hemorrhage (PPH) following cesarean delivery in women with a scarred uterus.
Methods: A total of 4,637 pregnant women with scarred uterus who underwent a cesarean delivery at a large hospital between January 2014 and December 2017 were enrolled. The women were divided into PPH (n = 287) and non-PPH (n = 4,350) groups. A model to predict PPH (blood loss ≥1,000 mL within 24 h following cesarean delivery) was developed using multivariate logistic regression analysis. Receiver operating characteristic curve was drawn, and the area under curve (AUC) was calculated.
Results: The incidence of PPH was 6.19% (287 of 4,637 women). Seven independent risk factors were associated with PPH: maternal age (odds ratio [OR] = 1.42, 95% confidence interval [CI]: 1.02–1.97), previous gravidity (OR = 1.24, 95% CI: 1.01–1.50), placental location (posterior wall of uterus, OR = 0.69, 95% CI: 0.47–1.02; other locations, OR = 1.21, 95% CI: 0.81–1.80), placenta previa (incomplete placenta previa, OR = 10.51, 95% CI: 5.99–18.42; complete placenta previa, OR = 31.65, 95% CI: 21.07–47.54), placenta accreta (OR = 6.39, 95% CI: 4.02–10.16), hypertensive disorders of pregnancy (OR = 2.27, 95% CI: 1.40–3.68), and fetal position (breech position, OR = 2.07, 95% CI: 1.19–3.60; transverse position, OR = 1.07, 95% CI: 0.48–2.41). A predictive model with AUC of 0.89 was developed (95% CI: 0.86–0.91, P < 0.001).
Conclusions: A model was developed to predict PPH following cesarean delivery in women with a scarred uterus.

Keywords: Cesarean Delivery; Logistic Regression; Postpartum Hemorrhage; Prediction; Risk Factors


How to cite this article:
Chen BN, Wang D, Li JP, Zhang LY, Qiao C. Postpartum hemorrhage following cesarean delivery in women with a scarred uterus: A retrospective cohort study. Reprod Dev Med 2019;3:230-4

How to cite this URL:
Chen BN, Wang D, Li JP, Zhang LY, Qiao C. Postpartum hemorrhage following cesarean delivery in women with a scarred uterus: A retrospective cohort study. Reprod Dev Med [serial online] 2019 [cited 2020 Feb 27];3:230-4. Available from: http://www.repdevmed.org/text.asp?2019/3/4/230/274548




  Introduction Top


Postpartum hemorrhage (PPH), a serious postpartum complication, has a high mortality rate and is life-threatening in several cases. According to the World Health Organization statistics, approximately one-quarter of all maternal deaths were caused by bleeding.[1] Previous cesarean section and myomectomy are the primary causes of a scarred uterus in pregnant women and are also reported to be risk factors for PPH.[2],[3],[4] In other words, the risk of PPH is higher in pregnant women with scarred uterus. Prediction of PPH in this population is of considerable practical significance to prevent and control PPH. In this study, we analyzed the risk factors for PPH following cesarean delivery in women with a scarred uterus and proposed a model to predict the risk of PPH.


  Methods Top


Participants and sampling

A total of 4,637 pregnant women with a scarred uterus who underwent a cesarean delivery at a large hospital were enrolled between January 2014 and December 2017. Inclusion criteria were as follows: previous cesarean section or myomectomy, cesarean delivery in this pregnancy, and singleton. Exclusion criteria were as follows: intrauterine fetal demise, gestational age at delivery <20 weeks, and neonatal weight <500 g. The women were divided into PPH (n = 287) and non-PPH (n = 4,350) groups.

This study was approved by the Medical Ethics Committee of China Medical University. Based on similar studies and inclusion criteria for this study,[5],[6],[7] we collected the following information: maternal age, previous gravidity, number of previous cesarean deliveries, vaginal bleeding times, placental location, placenta previa, placenta accreta, placental residue, hypertensive disorders of pregnancy, fetal position, gestational age in weeks at delivery, neonatal weight, multiple pregnancy, intrauterine fetal demise, and PPH. All data were obtained from the hospital electronic database. Based on the American College of Obstetrics and Gynecology (ACOG) Practice Bulletin,[8] PPH was defined as blood loss of more than 500 mL within 24 h following vaginal delivery and more than 1,000 mL following cesarean delivery. Hypertensive disorders of pregnancy and placental accreta were also defined according to the ACOG criteria.[9],[10] Moreover, placental residue was defined as the placenta was still not completely discharged and a part of it remained in the uterus after the fetus had delivered for 30 minutes.[11] A complete placenta previa involved the complete coverage of the internal os, a partial placenta previa involved the placental tissue covering only part of the internal os, and a marginal placenta previa involved the lower edge of the placenta reaching the internal os.[12]

Statistical methods

A multicollinearity diagnosis was performed using variance inflation factor and condition index. Kolmogorov–Smirnov test was used to assess the distribution of continuous variables. A bivariate analysis was conducted using Mann–Whitney U and Fisher's exact tests for continuous variables and categorical variables, respectively. Multivariate logistic regression analysis was used to identify independent risk factors and generate a predictive model. Receiver operating characteristic (ROC) curve was drawn, and the area under curve (AUC) was calculated. All data analyses were conducted using SPSS version 25.0 (IBM Corp., Armonk, NY, USA).


  Results Top


In this study, 287 of 4,637 pregnant women had PPH with an incidence of 6.19%. The data distribution characteristics and univariate analysis results of the PPH and non-PPH groups are shown in [Table 1] and [Table 2]. There was no multicollinearity among the factors. There were significant differences in maternal age, previous gravidity, vaginal bleeding times, placental location, placenta previa, placenta accreta, placental residue, hypertensive disorders of pregnancy, fetal position, gestational age at delivery, and neonatal weight (P < 0.05). The proportion of pregnant women who were ≥35 years old in the PPH group (48.8%) was higher than that in the non-PPH group (31.4%), which showed that older women were more likely to have PPH. It could also be seen from the [Table 1] that the proportion of pregnant women who have a higher previous gravidity (≥3) in the two groups is also significantly different (PPH group: 44.9%; non-PPH group: 23.9%). Moreover, the median of gestational weeks at delivery of non-PPH group (36.07) is higher than that in PPH-group (38.00).
Table 1: Distribution characteristics of related factors

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Table 2: Placental- and fetal-related factors

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On multivariate logistic regression analysis, seven independent risk factors were identified to be associated with PPH: maternal age, previous gravidity, placental location, placenta previa, placenta accreta, hypertensive disorders of pregnancy, and fetal position. A predictive model was developed: Logit (P) = −4.517 + 0.349 × MA + 0.211 × PG + (−0.365 × PW + 0.187 × OL) + (2.352 × IPP + 3.455 × CPP) + 1.855 × PA + 0.818 × HDP + (0.728 × BP + 0.069 × TP). The meanings of the abbreviations in this formula and the assignment under different circumstances are shown in [Table 3]. The model had a good ROC curve [Figure 1] with an AUC of 0.89 (95% confidence interval [CI]: 0.86–0.91, P < 0.001).
Table 3: Meanings of abbreviations and assignment

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Figure 1: Receiver operating characteristic curve of the predictive model.

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  Discussion Top


PPH remains one of the most important etiologies of maternal mortality.[13] Studies have shown that the incidence of PPH is approximately 2%–3%.[4] However, in this study, we found an incidence of 6.19%, which is higher than the average incidence. This may be because the effect of cesarean section on the uterus is greater than that of vaginal delivery. In addition, the presence of uterine scarring affects placental development,[14] which can increase the possibility of uterine atony and placenta-related diseases, as well as increase the risk of PPH.

In the logistic regression analysis, we found that maternal age, previous gravidity, fetal position, hypertensive disorders of pregnancy, and placental-related factors (placental location, placenta previa, and placenta accreta) were independent risk factors for PPH.

The results of this study on maternal age and previous gravidity are similar to those of other studies.[15],[16] Older women (age ≥35 years) and women with a large number of previous pregnancies are more likely to experience uterine atony, which increases the risk of PPH.

The relationship between fetal position and PPH has rarely been reported. However, it has been reported that the transverse position of the fetus can increase the incidence of PPH.[17] This study found that women with breech or transverse position had a higher risk of PPH than those with vertex position. This may be because breech and transverse positions are more likely to cause difficulty in delivery of the fetus during cesarean section,[18] which has a negative impact on postpartum uterine contractions and increases the risk of PPH.

Hypertensive disorders of pregnancy are independent risk factors for PPH and have been reported previously.[19] These disorders can cause severe vascular endothelial damage because of small blood vessel spasm, and the damage is irreversible and progressively worsens with age.[20] This causes abnormal coagulation and uterine ischemia, resulting in PPH.

Placenta previa and placenta accreta are risk factors for PPH. As early as 1997, there were reports of PPH caused by placenta previa.[21] The incidence of PPH in women with placenta previa in Asia is as high as 20.7%.[22] Studies have shown that previous cesarean section is one of the three major factors that cause placenta accreta during subsequent pregnancies.[23] In this study, the incidence of PPH in women with placenta previa (52.3%) or placenta accreta (70.4%) was high. This suggests that these conditions are more likely to cause PPH in women with a scarred uterus, and such women should be paid more attention in clinic.

We found that placental attachment to the posterior wall of the uterus (odds ratio [OR] = 0.69, 95% CI: 0.47–1.02), as opposed to the anterior wall or any other location, could reduce the risk of PPH. These findings are consistent with those of existing research.[24]

There are some limitations to this study. Errors and deficiencies in the information recording process caused bias in this retrospective study. Moreover, the hospital in which this study was conducted is a large regional referral center, and many disorders, such as placenta previa, placenta accreta, and hypertensive disorders of pregnancy, are more severe than in average clinical locations, which may also cause bias in the study. In addition, our predictive model lacks external validation. If the results of validation are ideal, the model can be applied and promoted for clinical use.

In summary, this study found seven independent risk factors for PPH following cesarean delivery among women with a scarred uterus. In addition, we developed a model to predict the risk of PPH in this population.

Financial support and sponsorship

This work was supported by the National Key R&D Program of China (2016YFC1000404), the National Natural Science Foundation of China (General Program; 81370735), the National Natural Science Foundation of China (General Program; 81771610), and the Outstanding Scientific Fund of Shengjing Hospital (201706).

Conflicts of interest

There are no conflicts of interest.



 
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    Tables

  [Table 1], [Table 2], [Table 3]



 

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