Prediction of postpartum hemorrhage in women with gestational hypertension or mild preeclampsia at term

Koopmans, Corine M.; Tuuk, Karin van der; Groen, Henk; Doornbos, Johannes P.R.; Graaf, Irene M. de; Salm, Paulien C.M. van der; Porath, Martina; Kuppens, Simone; Wijnen, Ella J.; Aardenburg, Robert; Loon, A.J. van; Akerboom, Bettina M.C.; Lans, Peggy J. A. van der; Mol, Ben Willem J.; Pampus, Maria G. van

doi:10.1111/aogs.12352

Cited by 44 publications

(72 citation statements)

References 29 publications

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“…Dehydrated pregnant women are vulnerable to hemodynamic instability caused by PPH. An imbalance between angiogenic and antiangiogenic factors in the maternal blood is associated with gestational HTN [26]. In addition, deficient platelet count and HTN aggravated blood loss and required transfusion [27].…”

Section: Discussionmentioning

confidence: 99%

Predicting peripartum blood transfusion: focusing on pre-pregnancy characteristics

Ouh

Lee

Ahn

et al. 2019

BMC Pregnancy Childbirth

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BackgroundObstetric hemorrhage is one of the most common causes of obstetrical morbidity and mortality, and transfusion is the most important management for hemorrhage. The aim of our study was to investigate the pre-pregnancy and pregnancy risk factors for peripartum transfusion.MethodsWomen who delivered a baby from 2010 to 2014 in Korea and participated in the Korean National Health Screening Program for Infants and Children were included. To analyze pre-pregnant risk factors for peripartum transfusion, an additional analysis was done for women who underwent a National Health Screening Examination within 1 year before pregnancy, including maternal waist circumference, body mass index, blood pressure, laboratory tests and history of smoking. Multivariable logistic regression analysis was used to estimate the risk factors for peripartum transfusion.ResultsOf the total 1,980,126 women who met the inclusion criteria, 36,868 (1.86%) were transfused at peripartum. In a multivariable regression model, the pregnancy risk factors for peripartum transfusion included maternal age above 35 years [odds ratio (OR): 1.41; 95% confidence interval (CI): 1.32–1.50], preterm birth (OR: 2.39; 95% CI: 2.15–2.65), and maternal hypertension (OR: 2.49; 95% CI: 2.24–2.77). Pre-pregnancy risk factors including fasting glucose level of more than 126 mg/dL (OR: 1.11; 95% CI: 1.02–1.20), current-smoker status (OR: 1.20; 95% CI: 1.06–1.37), and waist-circumference less than 80 cm (OR: 1.18; 95% CI: 1.06–1.30) were independently associated with peripartum blood transfusion.ConclusionsSeveral pre-pregnancy and pregnancy risk factors were associated with peripartum blood transfusion. Some identified factors are modifiable before conception, and our study validated peripartum blood transfusion as a form of triage.

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Section: Discussionmentioning

confidence: 99%

Predicting peripartum blood transfusion: focusing on pre-pregnancy characteristics

Ouh

Lee

Ahn

et al. 2019

BMC Pregnancy Childbirth

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“…Also, women with known risk factors for postpartum hemorrhage frequently do not bleed excessively following childbirth. It has therefore proven difficult to develop a reliable prediction model for postpartum hemorrhage based on clinical peripartum risk factors …”

Section: Introductionmentioning

confidence: 99%

“…It has therefore proven difficult to develop a reliable prediction model for postpartum hemorrhage based on clinical peripartum risk factors. 5,8,9 In general clinical practice, assessment of bleeding risk is performed by assessing clinical history, performing a physical examination, and sometimes the use of screening coagulation tests. 10,11 However, coagulation testing to predict bleeding risk prior to invasive procedures was found to be not useful due to limited sensitivity and specificity of the tests and low prevalence of bleeding disorders.…”

Section: Introductionmentioning

confidence: 99%

Predictive value of a bleeding score for postpartum hemorrhage

Gillissen

Akker

Caram‐Deelder

et al. 2019

Research and Practice in Thrombosis and Haemostasis

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Background A reliable screening tool that could contribute to the identification of women with an increased risk of postpartum hemorrhage would be of great clinical significance. Objectives The aim of this study was to examine the added predictive value of a bleeding assessment tool for postpartum hemorrhage exceeding 1000 mL . Patients/Methods Prospective two‐center cohort study among 1147 pregnant women visiting the outpatient clinic or the maternity ward who completed a bleeding assessment tool prior to birth. The condensed MCMDM ‐1 VWD bleeding assessment tool was adjusted to a questionnaire that could be used as a self‐assessment bleeding tool. A score of ≥4 was considered to be abnormal. Results In the 1147 pregnant women in our cohort, bleeding scores ranged from −3 to 13, with a median of 1 ( IQR −1 to 3); 197 (17%) women developed postpartum hemorrhage. Among women with a history of postpartum hemorrhage 29% developed postpartum hemorrhage. Among 147 women with an abnormal bleeding score (≥4), 27 (18%) developed postpartum hemorrhage, whereas the remaining 170 cases of postpartum hemorrhage had a normal bleeding score. Despite the high incidence of postpartum hemorrhage, the ability of the bleeding score to predict postpartum hemorrhage was poor: area under receiver operating curve 0.53 (95% CI 0.49‐0.58) for postpartum hemorrhage ( PPH ) ≥1000 mL . Conclusions A history of significant postpartum hemorrhage was associated with an increased risk of subsequent postpartum hemorrhage. However, screening with a bleeding assessment tool did not help to discriminate women who will develop postpartum hemorrhage from women who will not.

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“…However, although a large body of evidence has established numerous risk factors for postpartum haemorrhage, studies using clinical data to build predictive models have resulted in poor predictive validity, failed properly to validate the models in independent samples, or poorly defined the outcome. [19][20][21][22][23][24] Existing predictive models of postpartum sepsis have focused on highly specific patient subpopulations, such as mothers presenting to the emergency room (ER) with suspected sepsis and mothers diagnosed with chorioamnionitis, 25,26 with results difficult to judge in the absence of validation.…”

Section: Main Findingsmentioning

confidence: 99%

Predicting common maternal postpartum complications: leveraging health administrative data and machine learning

Betts

Kisely

Alati

2019

BJOG

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Objective We aimed to predict the risk of common maternal postpartum complications requiring an inpatient episode of care. Design and setting Maternal data from the beginning of gestation up to and including the delivery, and neonatal data recorded at delivery, were used to predict postpartum complications. Sample Administrative health data of all inpatient live births (n = 422 509) in the Australian state of Queensland between January 2009 and October 2015. Method Gradient boosted trees were used with five‐fold cross‐validation to compare model performance. The best performing models for each outcome were then assessed in the independent validation data using the area under the receiver operating curve (AUC‐ROC). Main outcome measure Postpartum complications occurring in the first 12 weeks after delivery requiring hospital admission. Results Postpartum hypertensive disorders obtained good discrimination in the independent validation data (AUC = 0.879, 95% CI 0.846–0.912), as did obstetric surgical wound infection (AUC = 0.856, 95% CI 0.838–0.873), whereas postpartum sepsis and haemorrhage obtained poor discrimination. Conclusions Our study suggests that routinely collected health data have the potential to play an important role in helping determine women's risk of common postpartum complications leading to hospital admission. This information can be presented to clinical staff after delivery to help guide immediate postpartum care, delayed discharge, and post‐discharge patient follow up. For such a system to be effective and valued, it must produce accurate predictions, and our findings suggest areas where routine data collection could be strengthened to this end. Tweetable abstract Improved prediction of maternal postnatal hypertensive disorders and wound infection via machine learning.

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Prediction of postpartum hemorrhage in women with gestational hypertension or mild preeclampsia at term

Cited by 44 publications

References 29 publications

Predicting peripartum blood transfusion: focusing on pre-pregnancy characteristics

Predicting peripartum blood transfusion: focusing on pre-pregnancy characteristics

Predictive value of a bleeding score for postpartum hemorrhage

Predicting common maternal postpartum complications: leveraging health administrative data and machine learning

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