Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores

摘要 产后出血，定义为分娩后24小时内估计失血量至少1000 mL， 这是产妇发病率和死亡率高的主要原因。每年，产后出血并发症2-3% 占所有怀孕的10%，并造成全球14万例孕产妇死亡。在美国， 显著的种族差异：黑人妇女与黑人妇女相比， 非黑人女性。虽然已经开发了临床产后出血风险预测工具，但它们未能 识别高达40%的病例;因此，目前临床上没有广泛采用基于证据的预测工具 实践因此，迫切需要一种高效、精确和个性化的产后出血风险预测工具。 needed.最近，机器学习方法越来越多地用于开发准确的预测 与传统的统计方法相比具有上级性能的模型，并发现新的 预测器，几乎没有预先指定。此外，可解释的机器学习方法允许 透明决策和减少偏见。通过这种方式，机器学习模型可能会导致更多 准确的产后出血预测比目前现有的工具。此外，由于高达18%的 产后出血风险是家族性的，许多与产后出血相关的临床危险因素 出血有一个完善的多基因结构，使用多基因风险工具可以进一步提高 产后出血风险预测根据NIH IMPROVE倡议的目标， 和结果，我们建议在这里开发一个高保真算法，结合临床和遗传因素， 更准确地预测孕妇产后出血的风险。我们会利用我们的财富 患者数据库和最先进的计算工具，以：（1）开发一种改进的算法来分层患者 产后出血风险，重点是透明度和减少偏倚，以及（2）描述 产后出血风险的遗传因素。总的来说，这个项目将提高我们精确预测 通过对新预测因素的调查、临床之间的相互作用，有产后出血风险的患者 和遗传贡献者，以及机器学习和多基因风险评分在这些方面的新应用 结果。最终，我们的目标是在临床实践中验证和实施这些工具， 提高预防孕产妇发病和死亡的能力。通过完成这些目标，我将制定一个 建立我的研究轨迹和过渡到独立作为一个医生的特定技能集必不可少- 科学家利用平移计算方法来预测和改善不良产科结果。

ABSTRACT Postpartum hemorrhage, defined as estimated blood loss of at least 1000 mL within 24 hours of delivery, is the leading cause for severe maternal morbidity and mortality. Annually, postpartum hemorrhage complicates 2-3% of all pregnancies and accounts for 140,000 maternal deaths globally. In the United States, there are also significant racial disparities: Black women have a five-fold higher risk of hemorrhage-related death compared to non-Black women. 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 the 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 reduction of bias. 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 individuals. 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 bias reduction, and (2) delineate the contribution of the 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 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.