Cases of Postpartum Hemorrhage and Hysterectomy in Thailand’s Northern and Northeastern Provincial Hospitals

Abstract

PPH is a major cause of maternal death. Hysterectomy is safe to treat uncontrollable PPH. However, it may not be the best option for women who want to have children. The risk score tool to detect PPH earlier is needed in low-resource cities such as Chiang Rai and Sakon Nakhon province. This study aims to perform a risk score tool to prevent PPH in the northern and northeastern hospitals in Thailand; using mixed methods, identify risk factors for PPH from 20 articles globally and in Thailand using Med Calc, and develop the tool for prediction of PPH; and tool testing and a one-year follow-up on PPH-related hysterectomy cases. Results showed that this risk score tool can detect PPH earlier, reducing the number of PPH and hysterectomy cases. This risk score tool needs to be implemented in the same situations as hospitals to save pregnant women’s lives.

1. Introduction

Hemorrhage is the cause of 12.0–18.0% of deaths during pregnancy [1, 2, 3]. Severe postpartum hemorrhage (PPH) is a major cause of maternal mortality and morbidity [4, 5] and is increasing in incidence worldwide [6, 7], especially in low resource countries [8]. Emergency hysterectomy is increasingly performed to treat uncontrollable PPH [1, 2, 3]. It was performed at the time of, or within 24 h of, a vaginal or abdominal delivery for the treatment of hemorrhage that was unresponsive to unservative approaches [9]. Variability in the incidence of PPH-related hysterectomy is different in various countries and even among institutions [9, 10, 11, 12, 13].

According to recent reports, 0.20–5.09 of every 1000 postnatal women across the globe have undergone an emergency hysterectomy [14]. Hysterectomy is considered to be a safe, low-risk surgery. It is, by nature, unplanned and performed expeditiously in the case of severe PPH. It may not be the best option for all women, especially those who still want to have children. Some people may have an adverse reaction to the anesthetic, heavy bleeding, and infection around the incision site [15].

The guidelines of the World Health Organization (WHO) aim to prevent and manage PPH by active management of the third stage of labor (AMTSL) [16]. Thai government policy to prevent PPH in 2013 was involved in the project—Every Woman Every Child (EWEC) to decrease maternal mortality and child mortality by 16 million cases in 2015 [17, 18]. However, the incidence of PPH was increased from 2.30 to 2.65% from 2009 to 2015 [19]. In low-resource city with various ethnic groups, surrounded by mountains and forests as in Chiang Rai province and Sakon Nakhon province [20, 21]. The incidence of PPH is increasing in Chiang Rai from 1.12 to 2.07%, but maternal death from PPH decreased from 3.05 to 1.23% during 2012-2015 [20]. In the fiscal year 2014–2015, PPH-related hysterectomy decreased in number from 2 cases to 1 case [20]. In Sakon Nakhon, during 2015–2018 the incidence of PPH is about 1.13–1.39%. The maternal deaths were decreased from 33.83 to 27.84 per 100,000 infant live births. However, it was higher than the standard criterion of 17.0 per 100,000 infant live births [21].

A tool developed from significantly high-risk factors [22, 23, 24] associated with PPH was performed in western societies and Thailand [25, 26, 27, 28, 29]. These tools can detect PPH earlier and can reduce the number of maternal deaths and PPH-related hysterectomies in Thailand [20, 21].

This study aimed to synthesize knowledge about the early management of PPH, summarize the appropriate risk score tool for the prediction of PPH, and reduce the number of maternal deaths and PPH-related hysterectomy cases in two lower resource cities in the north and northeast of Thailand.

2. Objectives

The objective of this study was to synthesize knowledge about the early management of PPH and an appropriate risk score tool to reduce PPH-related hysterectomy cases in two lower resource cities in the north and northeast of Thailand.

3. Methodologies

The study reviewed the results of the author’s research in four steps as follows:

Step 1: Using meta-analysis, we reviewed the risk factors for PPH during vaginal deliveries in 20 articles published in Thailand and around the world between 2005 and 2017.

Step 2: Reviewed the research results of the risk scoring system for the prediction of postpartum blood loss over 300 mL at Chiang Rai Regional Hospital, Thailand.

Step 3: Reviewed the research results of an appropriate risk score tool for the prediction of PPH at Sakon Nakhon Hospital, Thailand.

Step 4: During the years 2019–2020, the number of maternal deaths and PPH-related hysterectomies at Chiang Rai Regional Hospital and Sakon Nakhon province were reported.

The research review was approved by the Ethics Committee for Human Research at Khon Kaen University, Thailand [HE 601234, HE 611093], the Chiang Rai Regional Hospital Ethics Committee on July 21st, 2017, and the Ethics committee of Sakon Nakhon Hospital (SKHREC 422562). Most of the research was based on secondary data. Those who volunteered had signed a consent form.

4. Results

There are four steps to this research result as follows:

In Step 1, reviewed research results of risk factors for PPH via vaginal deliveries: systematic review and meta-analysis (Table 1) [29].

Risk factors	Subgroup	Statistical procedures
		Heterogeneity Test I^2	Estimate size (fixed effect)
			OR (95% CI)	P-value
I. Eight of high-risk factors (odds ratio > 2.0)
1. Antepartum hemoglobin level	>10 g/dL	95.71	4.80 (4.00–5.76)	<0.001
2. Coagulopathy		35.18	11.96 (2.64–54.10)	0.004
3. History of prior pregnancy and delivery	Prior PPH	40.89	4.01 (2.32–6.93)	<0.001
4. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Fibroid	89.97	0.73 (0.70–0.75)	<0.001
5. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Multiple pregnancy	51.23	2.69 (2.32–3.11)	<0.001
6. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Gestational hypertensive disorder	69.13	2.07 (1.72–2.50)	<0.001
7. Placenta factors	Placenta previa	28.44	5.01 (3.61–6.97)	<0.001
8. Placenta Factors	Placenta accrete	0.00	3.55 (1.84–6.86)	<0.001
II. Six moderate risk factors (odds ratio > 1.5–2.0)
1. Obstetric factors parity	Nulliparous	72.62	1.93 (1.53–2.43)	<0.001
2. Gestational age (large gestational age)	>40 weeks	47.19	1.35 (1.28–1.42)	<0.001
3. Placenta factors	Placenta abruption	39.13	1.70 (1.06–2.73)	0.029
4. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Chorioamnionitis	0.00	1.85 (1.45–2.98)	0.012
5. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Induction of labor	69.33	1.77 (1.57–2.00)	<0.001
6. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Augmentation of Labor	69.66	1.57 (1.35–5.87)	<0.001
III. Seven of low-risk factors (odds ratio > 1.0–1.5)
1. Individual factors maternal age	<20 years old	31.40	1.36 (1.26–1.46)	<0.001
2. Individual factors maternal age	>35 years old	17.37	1.32 (1.29–1.35)	<0.001
3. 1 Body mass index (BMI) level	<30 kg/m^2	0.00	1.17 (1.05–1.31)	0.050
3. 2 Body mass index (BMI) level	>30 kg/m^2	0.00	1.18 (1.01–1.38)	0.027
4. Obstetric Factors parity	Primiparous	97.64	1.29 (1.18–1.41)	<0.001
5. Gestational age	>42 weeks	47.19	1.35 (1.28–1.42)	<0.001
6. Complication of current pregnancy, 1st stage of labor, received procedure of 1st stage of labor	Gestational diabetes mellitus	0.00	1.35 (1.22–1.45)	<0.001
7. Complication of current pregnancy and 1st stage of labor receive procedure	Received analgesic drugs	10.03	1.38 (1.27–1.49)	<0.001

Table 1.

Med calc version 18.6 was used to analyze risk factors for PPH during vaginal deliveries.

Source: approved by I-Tuporn, et al. [29].

This study was analyzed and identified risk factors for PPH via vaginal deliveries from 20 articles from 2005 to 2017 in Thailand and globally, using MedCalc version 18.2.1 and version 18.6 [30].

The results showed that 21 factors, including eight high-risk factors for PPH (odds ratio > 2.0) include antepartum hemoglobin ≤10 g/dL, coagulopathy, prior PPH, fibroid, placenta previa, placenta accrete, multiple pregnancy, and gestational hypertensive disorder. Six moderate risk factors for PPH (odds ratio > 1.5–2.0) include nulliparous status, large gestational age, placenta abruption, chorioamnionitis, induction, and augmentation of labor. Seven low-risk factors for PPH (odds ratio > 1.0–1.5) include maternal age < 20 years old and ≥ 35 years old, BMI level, primiparous, gestational age ≥ 42 weeks, gestational diabetes mellitus, and having received analgesic drugs.

In Step 2, Chiang Rai Regional Hospital reviewed the research findings of a risk scoring system for predicting postpartum blood loss greater than 300 mL (Figure 1) [31].

Figure 1.

Form for recording risk scores to predict postpartum hemorrhage (PPH) of blood loss over 300 ml after vaginal delivery. Source: approved by I-Tuporn, et al. [31].

The results showed that the eight predictors of I-Tuporn et al. [31] (Figure 1) from the cause of PPH (4 T’s and 7 steps of the clinical prediction model of Steyerberg) [32, 33] and by comparison with the standard monogram of Biguzzi [34], Sittipan [28], and Suta [27] could predict postpartum blood loss over 300 ml at Chiang Rai Regional Hospital with a sensitivity of 80.7%, a specificity of 60.8%, and the ROC curve equal to 0.71 at the optional cut-off score of four marks or above (see Figure 1) [31].

In Step 3, we reviewed research results for an appropriate assessment of PPH by. using a risk score tool for prediction at Sakon Nakhon Hospital, Thailand (Table 2) [35].

Level of blood loss	ROC curve	Sensitivity (%)	Specificity (%)	Accuracy (%)	Positive predictive value (%)	Negative predictive value (%)	95% CI	P-Value
>250 ml	0.627	57.33	61.95	59.84	58.01	61.48	0.592–0.662	<0.001
>275 ml	0.608	15.69	92.92	56.04	66.96	54.66	0.554–0.662	<0.001
>300 ml	0.606	15.48	92.92	55.94	66.66	54.60	0.552–0.661	<0.001
>500 ml	0.653	5.02	98.66	53.94	77.41	53.19	0.563–0.744	0.004

Table 2.

Review of risk score at the different levels of blood loss from 250 ml. to 500 ml. in 1001 cases who underwent vaginal delivery at Sakhon Nakhon hospital, Thailand, during June 2018 to December 2019.

Source: Approved by Nutravong et al. [35], on An Appropriate Assessment of PPH by using a Risk Score Tool for prediction at Sakon Nakhon, Hospital, Thailand oral presentation in the International Webinar on Primary Healthcare and Medicare held during November 08–09, 2021/Vienna Austria.

It found that the eight predictors of I-Tuporn et al. [31] (Figure 1) can be used to predict early PPH in Sakon Nakhon Hospital since blood loss is 250 ml and over with a sensitivity of 57.33%, a specificity of 61.95%, and a ROC curve equal to 0.62 (Table 2 and Figure 2).

Figure 2.

The ROC curve’s performance at different levels of blood loss at over (a) 250 mL, (b) 275 mL, (c) 300 mL, and (d) 500 mL of a risk score for PPH prediction from 1001 cases after delivery at Sakon Nakhon hospital, Thailand from July 2018 to December 2019.

In Step 4, We reported the number of maternal deaths and PPH-related hysterectomy at Chiang Rai Regional Hospital and Sakon Nakhon province during 2019–2020.

The results of one-year follow-up showed the incidence of Chiang Rai Regional Hospital.

The number of cases of PPH-related hysterectomy decreased from 4.61% to 3.81% from 2019 to 2020 report of. It had no cases of PPH-related hysterectomy but had reported no maternal death per 100,000 infant live births, 36.60 and 37.36 respectively.

In Sakon Nakhon province, the incidence of PPH decreased from 1.39 to 1.10%, but there was no report of PPH-related hysterectomy. The maternal death rate decreased from 27.84 to 15.12 per 100,000 live births, from 2018 to 2019 (Table 3).

Setting	Pregnancy Problems	Fiscal years
		2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Thailand*	Incidence of PPH	2.30%	2.37%	2.44%	2.40%	2.39%	2.54%	2.65%	NA	NA	NA	NA	NA
Chiang Rai** Province	Incidence of PPH	NA	NA	NA	1.12%	1.15%	1.34%	2.07%	NA	NA	NA	NA	NA
	PPH related Hysterectomy	NA	NA	NA	NA	NA	2	1 case	NA	NA	NA	NA	NA
	Maternal death/100,000 infant live birth	NA	NA	NA	3.05	4.58	1.82	1.23	NA	NA	NA	NA	NA
Chiang Rai** Regional Hospital	Incidence of PPH	NA	NA	NA	NA	9.0%	1.98%	2.64%	2.58%	2.61%	3.85%	4.61%	3.81%
	PPH related Hysterectomy	NA	NA	NA	NA	1 case	NA	NA	NA	NA	NA	NA	NA
	Maternal death/ 100,000 infant live birth	NA	NA	NA	NA	NA	36.14	18.09	NA	NA	17.45	36.60	37.36
Sakon Nakhon*** Province	Incidence of PPH	NA	NA	NA	NA	NA	NA	1.13%	0.93%	1.00%	1.39%	1.10%	NA
	PPH related Hysterectomy	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
	Maternal death/100,000 infant live birth	NA	NA	NA	NA	NA	NA	33.83	17.68	26.88	27.84	15.12	NA

Table 3.

Statistics on PPH, PPH-related hysterectomy, and maternal deaths were collected in Thailand’s Chiang Rai Province. Chiang Rai regional hospital, Sakon Nakhon Province.

^*

Medical Department, Ministry of Public Health document 2013 [17].

^**

Ajalapung, 2015 [20].

^***

Statistic Report for NE Thailand, 2019 [21].

Remarks: The standard Criterion of maternal deaths was 17 per 100,000 infant live birth.

5. Discussions

The postpartum hemorrhage (PPH) in I-Tuporn et al. [31] study, which was conducted in the Chiang Rai Regional Hospital in 2017, was 2.61%. It was lower than the study in Chonburi Hospital (4.95%) [28] and Maharat Nakorn Ratchasima Hospital (6.67%) [26], but it was related to the report of Bhumibol Adulyadej Hospital (1.98%) [25] and the report of Calvert et al. [36] that presented Asia’s regional PPH rate of 1.90% [36].

Emergency hysterectomy for the treatment of severe hemorrhage from vaginal delivery was not reported widely in Chiang Rai Regional Hospital or Sakon Nakhon province. It was presented only some years ago and reported only a few cases. However, the maternal death rate in Sakon Nakhon province from 2015 to 2018 was higher than the standard criterion of 17 per 100,000 infant live births. It was decreased in the year 2019 to 15.12 per 100,000 infant live births after this hospital used a risk score tool with 8 predictors by I-Tuporn et al. [31] to detect earlier PPH and early treatment as blood loss over 250 mL from the collector bag.

The risk score tool for the prediction of PPH in Thailand had five studies [25, 26, 27, 28, 29]. They were developed in different settings. They had some similar risk factors for the detection of PPH. The study of I-Tuporn et al. [31] developed the risk score tool with 8 predictors, covering the cause of PPH (4 T’s and 7 steps of the clinical prediction model of Steyerberg [32, 33] and by comparison with the standard monogram of Biguzzi [34], Sittipan [28], and Suta et al. [27]) I-Tuporn et al. [31] risk score tool could be used in low-resource cities with various ethnic groups, as in Chiang Rai and Sakon Nakhon province, which are in the north and northeast of Thailand, respectively.

The results of the Chiang Rai and Sakon Nakhon provinces study after 1 year of follow-up showed that the maternal death rate in Sakon Nakhon province had decreased to normal criterion, and there were no reports of PPH-related hysterectomy cases in these two provinces.

6. Conclusions

In conclusion, the problems of PPH concerned the Thai government. Many projects were carried out in accordance with World Health Organization’s (WHO) [16] guidelines to reduce PPH, PPH-related hysterectomy, and maternal death.

Due to some settings in Thailand, the government’s policy is not suitable for some women because of their low resources and distance from the cities. Some of the settings are surrounded by mountains and forests, and it is very hard to refer a pregnant woman with PPH to the provincial hospital. Most of them belong to different ethnic groups and cannot communicate with other people. Therefore, some of them die before seeing a doctor.

There should be a policy of early detection of PPH in those lower resource settings by using an appropriate risk score tool to predict the PPH risk for a pregnant woman’s life.

Acknowledgments

The authors would like to thank all of the respondents for their valuable contributions to this study and extend their special gratitude to the Department of Obstetrics and Gynecology in Chiang Rai Regional Hospital and Sakon Nakhon Hospital for the data support, and the Thai Society of Maternal and Fetal Medicine for funding support.

Conflict of interest

All authors declare that they have no conflicts of interest.