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\begin{document}

\title{Machine Learning for Prediction of Maternal Hemorrhage and Transfusion}



\author[1]{Homa Ahmadzia}%
\author[1]{Alexa Dzienny}%
\author[2]{Mike Bopf}%
\author[3]{Jerome Federspiel}%
\author[4]{Richard Amdur}%
\author[5]{Madeline Rice}%
\author[2]{Laritza Rodriguez}%
\affil[1]{The George Washington University School of Medicine and Health Sciences}%
\affil[2]{National Institutes of Health}%
\affil[3]{Duke University}%
\affil[4]{George Washington University}%
\affil[5]{The George Washington University}%


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  \date{May 28, 2024}


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\begin{abstract}
Objectives: To improve PPH prediction and to compare machine learning
and traditional statistical methods. Design: Cross-sectional Setting:
Deliveries across US hospitals Population: Deliveries across 12 US
hospitals from the 2002-2008 Consortium for Safe Labor dataset Method:
We developed models using the Consortium for Safe Labor dataset. Fifty
antepartum and intrapartum characteristics and hospital characteristics
were included. Logistic regression, support vector machines, multi-layer
perceptron, random forest, and gradient boosting were used to generate
prediction models. Receiver operating characteristic area under the
curve (ROC-AUC) and precision/recall area under the curve (PR-AUC) were
used to compare performance. Main Outcome Measure: The primary outcome
was transfusion of blood products or PPH (estimated blood loss
[?]1,000mL). The secondary outcome was transfusion of any blood products.
Results: Among 228,438 births, 5,760 women (3.1\%) had a postpartum
hemorrhage, 5,170 women (2.8\%) had a transfusion, and 10,344 women
(5.6\%) met criteria for the transfusion-PPH composite. Models
predicting transfusion-PPH composite using antepartum and intrapartum
features had the best positive predictive values with the gradient
boosting machine learning model performing best overall (ROC-AUC=0.833,
95\% CI {[}0.828-0.838{]}; PR-AUC=0.210 95\% CI {[}0.201-0.220{]}). The
most predictive features in the gradient boosting model predicting
transfusion-PPH composite were mode of delivery, oxytocin incremental
dose for labor(mU/min), intrapartum tocolytic use, presence of
anesthesia nurse, and hospital type. Conclusion: Machine learning offers
higher discrimination than logistic regression in predicting PPH. The
CSL dataset may not be optimal for analyzing risk due to strong subgroup
effects, which decreases accuracy and limits generalizability.%
\end{abstract}%



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