Prediction of Maternal Hemorrhage: Using Machine Learning to Identify Patients at Risk
Authors: Westcott Jill M. , Hughes Francine , Liu Wenke , Grivainis Mark , Fenyö David 
Published: June 2020

Background

Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States.

Objective

To utilize machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery.

Study Design

Women aged 18 to 55 delivering at a major academic center from July 2013 to October 2018 were included for analysis (n = 30,867). A total of 497 variables were collected from the electronic medical record including demographic information, obstetric, medical, surgical, and family history, vital signs, laboratory results, labor medication exposures, and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of 1000 mL at the time of delivery, regardless of delivery method, with 2179 positive cases observed (7.06%).

Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (n = 21,606) and validation (n = 4,630) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (n = 4,631) determined final performance by assessing for accuracy, area under the receiver operating curve (AUC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus limited to data available prior to the second stage of labor/at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery.

Results

Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUC 0.979, 95% CI 0.971–0.986 vs. AUC 0.955, 95% CI 0.939–0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned ≥ models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination, but lacked sensitivity necessary for clinical applicability.

Conclusions

Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete datasets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.

Condensation

Machine learning methods can be successfully utilized to predict nearly three-quarters of women at risk of postpartum hemorrhage when undergoing obstetric delivery.

AJOG at a Glance

Why was the study conducted?

To determine patients at risk for postpartum hemorrhage using modern machine learning techniques on a robust data set directly derived from the electronic medical record

What are the key findings?

Using 28 predictor features, the model successfully classified 73.7% of patients who ultimately had a postpartum hemorrhage using information available prior to delivery

Many previously identified risk factors for postpartum hemorrhage were not included in the final model, potentially discounting their contribution to hemorrhage risk

Models stratified by delivery method achieved good to excellent discrimination but noted lower sensitivity and need further investigation

What does this study add to what is already known?

This study represents the largest cohort directly-derived from the electronic medical record to use machine learning techniques to identify patients at risk for postpartum hemorrhage