Author: admin

<strong>Background:</strong> Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians; its risk and the mode of delivery are important points of discussion before labor. Many high-risk factors associated with postpartum hemorrhage are known prior to delivery and are often unpreventable. Delivery plans are influenced by the patient’s medical history, their preferences, and clinical decision-making. Informed consent regarding known risk factors for postpartum hemorrhage will help guide delivery care plans and mitigate risk. Machine learning models have been used to predict postpartum hemorrhage; however, translation into clinical support tools is challenging. Shared decision-making discussions can be facilitated with machine learning model–based clinical support tools predicting postpartum hemorrhage requiring a transfusion. <strong>Objective:</strong> We sought to develop a machine learning model for prediction of postpartum hemorrhage requiring a transfusion. Specifically, we sought to evaluate the model’s accuracy in predicting a patient’s postpartum transfusion risk based on delivery mode, whether labor was induced, and the delivery indications for the purpose of antenatal clinical decision support. Model performance was evaluated on existing structured data and physician-reviewed datasets. <strong>Methods:</strong> A 10-year retrospective cohort of 62,521 births in a community health system was sampled. A convenience sample of 1734 patients was analyzed to predict blood transfusion rate based on delivery mode and delivery indications. XGBoost, random forest, and generalized linear models were trained and compared for performance. Datasets were evaluated using the best-performing XGBoost machine learning model. A prototype clinical support app for physician-patient transfusion risk assessment was developed using the best-performing clinically relevant XGBoost model. <strong>Results:</strong> A generalized linear model, random forest model, and XGBoost model were evaluated. The XGBoost model was trained with an existing dataset extracted from electronic medical records (n=1734). The area under the curve (AUC) was 0.71, precision-recall receiver operating characteristic curve (PR-ROC) was 0.82, and <i>F</i>₁ score was 0.80. Performance on a physician-reviewed dataset (n=1734) was as follows: AUC=0.705, PR-ROC=0.78, and <i>F</i>₁ score=0.809. Feature importance ranking and prediction were not clinically accurate for the pre-review dataset. <strong>Conclusions:</strong> Machine learning models are useful to determine an individual’s postpartum transfusion risk based on clinically variable and potentially modifiable factors, such as delivery mode, whether labor is induced, and delivery indications. In this study, the XGBoost model had a slightly higher AUC on structured data extracted from electronic medical records than the same dataset after physician review (AUC=0.71 and PR-ROC=0.82 vs AUC=0.705 and PR-ROC=0.78), but a slightly lower <i>F</i>₁ score (<i>F</i>₁=0.80 vs <i>F</i>₁=0.809). XGBoost machine learning models trained on clinician-reviewed data can be used to predict postpartum transfusion. Clinically relevant, physician-labeled datasets are important for supervised machine learning model training for use in clinical decision support tools. Further study and external validation are needed prior to clinical use.