Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores

Postpartum hemorrhage, defined as estimated blood loss of at least 1000 mL within 24 hours of delivery, is the leading cause of severe maternal morbidity and mortality. Annually, postpartum hemorrhage complicates 2-3% of all pregnancies and accounts for 140,000 maternal deaths globally. While clinical postpartum hemorrhage risk prediction tools have been developed, they fail to identify up to 40% of cases; as a result, no evidence-based prediction tool is currently widely adopted in clinical practice. Thus, an efficient, precise, and personalized postpartum hemorrhage risk prediction tool is urgently needed. Recently, machine learning approaches have been increasingly used to develop accurate predictive models with superior performance compared to traditional statistical approaches and to discover new predictors, with little prior pre-specification. Moreover, the explainable machine learning methods allow for transparent decision-making and ensure model generalizability. In this way, machine learning models may lead to more accurate postpartum hemorrhage prediction than currently existing tools. In addition, since up to 18% of postpartum hemorrhage risk is familial and many of the clinical risk factors associated with postpartum hemorrhage have a well-established polygenic architecture, using polygenic risk tools may further enhance postpartum hemorrhage risk prediction. In line with the NIH IMPROVE initiative goals to improve maternal safety and outcomes, we propose here to develop a high-fidelity algorithm, combining both clinical and genetic factors, to more accurately predict the risk of postpartum hemorrhage in pregnant women. We will leverage our rich patient database and state-of-the-art computational tools to: (1) develop an improved algorithm to stratify patient postpartum hemorrhage risk with a focus on transparency and high performance, and (2) delineate the contribution of genetics to postpartum hemorrhage risk. Overall, this project will advance our ability to precisely predict patients at risk for postpartum hemorrhage, with the investigation of novel predictors, interaction between clinical and genetic contributors, and novel application of both machine learning and polygenic risk scores to these outcomes. Ultimately, we aim to validate and implement these tools in clinical practice, leading to a greatly enhanced ability to prevent maternal morbidity and mortality. By completion of these aims, I will develop a specific skill set essential for establishing my research trajectory and transition to independence as a physician-scientist utilizing translational computational approaches to predict and improve adverse obstetric outcomes.