Factors affecting third-stage management and postpartum hemorrhage in planned midwife-led home and birth center births in the United States.
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ABSTRACT: Background
Postpartum hemorrhage (PPH) is a potential childbirth complication. Little is known about how third-stage labor is managed by midwives in the United States, including use of uterotonic medication during community birth. Access to uterotonic medication may vary based on credentials of the midwife or state regulations governing midwifery.Methods
Using data from the Midwives of North America 2.0 database (2004-2009), we describe the PPH incidence for women giving birth in the community, their demographic and clinical characteristics, and methods used by midwives to address PPH. We also examined PPH rates by midwifery credentials and by the presence of regulations for legal midwifery practice.Results
Of the 17 836 vaginal births, 15.9% had blood loss of over 500 mL and 3.3% had 1000 mL or greater blood loss. Midwives used pharmaceuticals to prevent or treat postpartum bleeding in 6.3% and 13.9% of births, respectively, and the rate of hospital transfer after birth was 1.4% (n = 247). In adjusted analyses, PPH was less likely when births occurred at home vs a birth center, if the midwife had a CNM/CM credential vs a CPM/LM/LDM credential, or if the woman was multiparous without a history of PPH or prior cesarean birth. PPH was more likely in states with barriers to midwifery practice compared with regulated states (OR: 1.26; 95% CI, 1.16-1.38).Conclusions
Women giving birth in the community experienced low overall incidence of PPH-related hospital transfer. However, the occurrence of PPH itself would likely be reduced with improved legal access to uterotonic medication.
SUBMITTER: Erickson EN
PROVIDER: S-EPMC8248515 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPostpartum hemorrhage (PPH) is the leading cause of direct maternal mortality globally and in Suriname. We aimed to study the prevalence, risk indicators, causes, and management of PPH to identify opportunities for PPH reduction.MethodsA nationwide retrospective descriptive study of all hospital deliveries in Suriname in 2017 was performed. Logistic regression analysis was applied to identify risk indicators for PPH (≥ 500ml blood loss). Management of severe PPH (blood loss ≥1,000ml or ≥500ml with hypotension or at least three transfusions) was evaluated via a criteria-based audit using the national guideline.ResultsIn 2017, the prevalence of PPH and severe PPH in Suriname was 9.2% (n = 808/8,747) and 2.5% (n = 220/8,747), respectively. PPH varied from 5.8% to 15.8% across the hospitals. Risk indicators associated with severe PPH included being of African descent (Maroon aOR 2.1[95%CI 1.3-3.3], Creole aOR 1.8[95%CI 1.1-3.0]), multiple pregnancy (aOR 3.4[95%CI 1.7-7.1]), delivery in Hospital D (aOR 2.4[95%CI 1.7-3.4]), cesarean section (aOR 3.9[95%CI 2.9-5.3]), stillbirth (aOR 6.4 [95%CI 3.4-12.2]), preterm birth (aOR 2.1[95%CI 1.3-3.2]), and macrosomia (aOR 2.8 [95%CI 1.5-5.0]). Uterine atony (56.7%, n = 102/180[missing 40]) and retained placenta (19.4%, n = 35/180[missing 40]), were the main causes of severe PPH. A criteria-based audit revealed that women with severe PPH received prophylactic oxytocin in 61.3% (n = 95/155[missing 65]), oxytocin treatment in 68.8% (n = 106/154[missing 66]), and tranexamic acid in 4.9% (n = 5/103[missing 117]).ConclusionsPPH prevalence and risk indicators in Suriname were similar to international and regional reports. Inconsistent blood loss measurement, varied maternal and perinatal characteristics, and variable guideline adherence contributed to interhospital prevalence variation. PPH reduction in Suriname can be achieved through prevention by practicing active management of the third stage of labor in every birth and considering risk factors, early recognition by objective and consistent blood loss measurement, and prompt treatment by adequate administration of oxytocin and tranexamic acid according to national guidelines.
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Project description:Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to predict a postpartum hemorrhage defined as a blood loss of > 1000 mL. We constructed five machine learning models and a deep learning model consisting of neural networks with two layers after applying the ensemble learning of five machine learning classifiers, namely, logistic regression, a support vector machine, random forest, boosting trees, and decision tree. For an evaluation of the performance, we applied the area under the curve of the receiver operating characteristic (AUC), the accuracy, false positive rate (FPR) and false negative rate (FNR). The importance of each variable was evaluated through a comparison of the feature importance calculated using a Boosted tree. A total of 9,894 patients who underwent vaginal birth were enrolled in the study, including 188 cases (1.9%) with blood loss of > 1000 mL. The best learning model predicted postpartum hemorrhage with an AUC of 0.708, an accuracy of 0.686, FPR of 0.312, and FNR of 0.398. The analysis of the importance of the variables showed that pregnant gestation of labor, the maternal weight upon admission of labor, and the maternal weight before pregnancy were considered to be weighted factors. Machine learning model can predict postpartum hemorrhage during vaginal delivery. Further research should be conducted to analyze appropriate variables and prepare big data, such as hundreds of thousands of cases.
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