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A Pragmatic Randomized Controlled Trial to Predict Postpartum Hemorrhage
Sponsor: Holly Ende
Summary
This research project aims to enhance the safety of childbirth by using advanced computer models to predict the risk of postpartum hemorrhage (PPH). PPH is a significant concern for mothers during and after delivery. Current risk assessment tools are basic and do not adapt to changing conditions. This study will investigate whether a new and recently validated model for predicting PPH, combined with a provider-facing Best Practice Advisory (BPA) regarding currently recommended strategies triggered by an increased predicted risk, can improve perinatal outcomes. This study will compare the current category based risk assessment tool with a new, enhanced prediction model which calculates risk based on 21 factors, automatically updates as new information becomes available during labor and, if elevated, provides a provider-facing Best Practice Advisory (BPA) recommending consideration of strategies that are institutionally agreed to represent high-quality practice. Investigators hypothesize that the enhanced care approach will result in improved perinatal outcomes. The goal of the study is to improve the wellbeing of mothers during childbirth by harnessing the power of modern technology and data analysis.
Official title: Logistic Regression Prediction Model vs. Standard of Care for Prediction of Postpartum Hemorrhage - A Pragmatic Randomized Controlled Trial
Key Details
Gender
FEMALE
Age Range
Any - Any
Study Type
INTERVENTIONAL
Enrollment
10000
Start Date
2025-01-01
Completion Date
2027-07
Last Updated
2025-10-01
Healthy Volunteers
No
Conditions
Interventions
Novel PPH Risk Prediction Model - Comparator Arm B
Patients in this group will receive the standard care risk assessment with the addition of a recently developed, novel PPH risk prediction model, which will automatically calculate a patient's numerical risk of hemorrhage based on 21 risk factors. Elevated risk of hemorrhage (\>=3% predicted risk), as predicted by the model, will be linked to clinical decision support, including a best practice advisory with recommendations presented to providers for consideration when they access the patient's electronic health record.
Locations (1)
Vanderbilt University Medical Center
Nashville, Tennessee, United States