Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores

使用机器学习和多基因风险评分进行个性化产后出血预测

基本信息

批准号：
10670427
负责人：
Vesela Kovacheva
金额：
$ 16.85万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10670427
关键词：
Adopted Algorithms Architecture Body mass index Cessation of life Clinical Complex Complication Data Databases Decision Making Decision Trees Electronic Health Record Genetic Genetic Risk Genotype Gestational Diabetes Goals Hemorrhage Hospitals Hour Hypertension Individual Investigation Laboratories Link Logistic Regressions Machine Learning Maternal Health Maternal Mortality Mentors Methods Modeling Nature Obesity Outcome Patients Performance Pharmaceutical Preparations Physicians Postpartum Hemorrhage Predictive Value Pregnancy Pregnancy Outcome Premature Birth Process Prolonged labor Publishing Research Resources Risk Risk Factors Scientist Specific qualifier value United States United States National Institutes of Health Woman billing data biobank black women clinical practice clinical predictors clinical risk collaborative environment computerized tools demographics epidemiology study evidence base genetic risk factor genome wide association study genomic locus high risk improved innovation machine learning method machine learning model maternal morbidity maternal outcome maternal safety medical schools multi-ethnic neural network novel obstetric outcomes patient stratification personalized predictions polygenic risk score prediction algorithm predictive modeling predictive tools pregnant prevent racial bias racial disparity risk prediction risk prediction model risk stratification severe maternal morbidity skills standard of care tool trait translational research program

项目摘要

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.

抽象的产后出血定义为产后 24 小时内估计失血至少 1000 mL，严重孕产妇发病和死亡的主要原因。每年，产后出血会导致 2-3% 的并发症发生占所有妊娠的总和，并导致全球 140,000 名孕产妇死亡。在美国，也有显着的种族差异：黑人女性因出血而死亡的风险是黑人女性的五倍非黑人女性。虽然临床产后出血风险预测工具已经开发出来，但它们未能识别高达 40% 的病例；因此，目前临床上还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、精准、个性化的产后出血风险预测工具。需要。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统计方法相比具有卓越性能的模型并发现新的预测变量，几乎没有预先指定。此外，可解释的机器学习方法允许透明的决策和减少偏见。通过这种方式，机器学习模型可能会带来更多比现有工具更准确的产后出血预测。此外，由于高达 18% 产后出血风险具有家族性，许多临床危险因素与产后出血有关出血具有完善的多基因架构，使用多基因风险工具可以进一步增强产后出血风险预测。符合 NIH IMPROVE 倡议的目标，以提高孕产妇安全和结果，我们在此建议开发一种结合临床和遗传因素的高保真算法，更准确地预测孕妇产后出血的风险。我们将利用我们的丰富患者数据库和最先进的计算工具：(1) 开发改进的算法对患者进行分层产后出血风险，重点是透明度和减少偏见，以及 (2) 描述贡献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的能力有产后出血风险的患者，通过研究新的预测因素、临床之间的相互作用和遗传贡献者，以及机器学习和多基因风险评分在这些方面的新颖应用结果。最终，我们的目标是在临床实践中验证和实施这些工具，从而大大提高提高预防孕产妇发病和死亡的能力。通过完成这些目标，我将制定一个对于建立我的研究轨迹和过渡为独立医生至关重要的特定技能- 科学家利用转化计算方法来预测和改善不良产科结果。