Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach

Mehrnoush, Vahid; Ranjbar, Amene; Etebarian, Akbar; Darsareh, Fatemeh; Shekari, Mitra; Jahromi, Malihe Shirzadfard

doi:10.1016/j.xagr.2023.100185

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…More blood transfusion was needed, which is rational. This is in accordance with previous studies [ 9 , 19 ]. The purpose behind the expanded risk of postpartum hemorrhage in anemic women is obscure.…”

Section: Discussionsupporting

confidence: 94%

Maternal and Neonatal Outcomes of Iron Deficiency Anemia: A Retrospective Cohort Study

Safarzadeh,

Banihashemi,

Montazeri

et al. 2023

Cureus

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Introduction: Understanding the outcomes of anemia in pregnancy is critical. Since no study has been conducted regarding the maternal and neonatal outcomes of iron-deficiency anemia in Hormozgan province of Iran, this study aims to assess the maternal and neonatal outcomes of iron-deficiency anemia in women who gave birth in Hormozgan province from January 2020 to January 2022. Methods: We retrospectively assessed all singleton pregnant women who gave birth at a tertiary hospital in Bandar Abbas, Hormozgan province, Iran, for two years. We divided all women into iron-deficiency anemic and non-iron-deficiency anemic women. Iron-deficiency anemia was defined as hemoglobin less than 10.5 mg/dl at the time of admission without any other hemoglobinopathy, such as sickle cell anemia or thalassemia. Using electronic patient records, data were extracted from the Iranian Maternal and Neonatal Network (IMaN Net), a valid national system. Since the information of birth under 24 weeks of gestation is not recorded in this system, we excluded all deliveries under 24 weeks of gestation. The outcome measures of the study were demographic factors (age, education, residency place, access to prenatal care, smoking), obstetrical factors (parity, labor induction, fetal presentation, mode of delivery), and maternal and neonatal outcomes (the incidence of preeclampsia, gestational diabetes, placenta abruption, postpartum hemorrhage, maternal need for blood transfusion, maternal need for intensive care unit, preterm birth, low birth weight, intrauterine growth retardation, birth asphyxia, stillbirth, and neonatal intensive care admission). Chi-square tests were used to compare differences between iron-deficiency anemic and non-iron-deficiency anemic women. Logistic regression models were used to assess the effect of iron-deficiency anemia on maternal and neonatal outcomes. The result was presented as odds ratio (OR) or adjusted odds ratio (aOR) after adjusting for covariates and a 95% confidence interval (CI). Results: The incidence of iron-deficiency anemia was 2.97%. Education and residency were among the demographic factors that differed significantly between groups. Iron-deficiency anemia was more frequent in those with higher education and women who lived in rural areas. In terms of obstetrical factors, method of delivery was the only significantly different factor between groups. Iron-deficiency anemic mothers had substantially more instrumental deliveries than non-iron-deficiency anemic mothers (4.3% vs. 0.8%), while the incidence of cesarean section was lower. Based on logistic regression in terms of maternal and neonatal outcomes, iron-deficiency anemic women had a substantially higher risk of the need for maternal blood transfusion (aOR: 6.54, 95%CI: 4.72-8.15), postpartum hemorrhage (aOR: 1.54, 95%CI: 0.71-2.11), preterm birth (aOR: 0.98, 95%CI: 0.45-1.13), low birth weight (aOR: 1.04, 95%CI: 0.78-2.01), intrauterine growth retardation (aOR: 1.30, 95%CI: 0.99-2.10), and neonatal intensive care admi...

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Section: Discussionsupporting

confidence: 94%

Maternal and Neonatal Outcomes of Iron Deficiency Anemia: A Retrospective Cohort Study

Safarzadeh,

Banihashemi,

Montazeri

et al. 2023

Cureus

Self Cite

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“…All candidate models were trained, tested, and validated on the same data separation. As recommended by Mehrnoush et al [25], while various separations and approaches for training and test data can be utilised, it is crucial to maintain consistent separation across all algorithms for meaningful comparisons.…”

Section: Discussionmentioning

confidence: 99%

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Holcroft,

Karangwa,

Little

et al. 2024

IJERPH

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Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case–control study in northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic regression with elastic-net regularisation, Random Forests, Extremely Randomised Trees, and gradient-boosted trees with XGBoost. The Random Forest model, with an average sensitivity of 80.7%, specificity of 71.3%, and a misclassification rate of 12.19%, outperformed the other models, demonstrating its potential as a reliable tool for predicting PPH. The important predictors identified in this study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH prediction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions.

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“…This decline may indicate a potential issue of overfitting in the latter classifiers (23,24). In contrast, recent study on Iran population found that machine learning model such as random forest and decision tree provided improved performance in predicting PPH (25). Westcott et al (26) also reported better performance of boosted decision trees for prediction of PPH in United States population.…”

Section: Main Findingsmentioning

confidence: 95%

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Shah

Saxena

Rani

et al. 2023

Front. Glob. Womens Health

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IntroductionPostpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort.MethodFour machine learning models – logistic regression, naïve Bayes, decision tree, and random forest – were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve.ResultThe naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population.DiscussionThis study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

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Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach

Cited by 17 publications

References 22 publications

Maternal and Neonatal Outcomes of Iron Deficiency Anemia: A Retrospective Cohort Study

Maternal and Neonatal Outcomes of Iron Deficiency Anemia: A Retrospective Cohort Study

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

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