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：
10524826
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/10524826
关键词：
Adopted Algorithms Architecture Body mass index Cessation of life Chiroptera Clinical Complex Complication Data Databases Decision Making Decision Trees Electronic Health Record Genetic Genetic Risk Genotype Gestational Diabetes Goals Hemorrhage Hospitals Hour Hypertension Individual Institutes Investigation Laboratories Lead Link Logistic Regressions Machine Learning Maternal Health Maternal Mortality Medical Genetics 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 United States United States National Institutes of Health base billing data biobank black women clinical practice 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 safety medical schools multi-ethnic neural network novel obstetric outcomes patient stratification personalized predictions polygenic risk score prediction algorithm predictive modeling pregnant prevent racial bias racial disparity risk prediction risk stratification safety outcomes 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在分娩后24小时内至少1000毫升的估计失血量是严重孕产妇发病率和死亡率的主要原因。每年，产后出血使2-3％复杂化在所有怀孕中，全球造成140,000人死亡。在美国，也有种族差异很大：黑人妇女与出血相关死亡的风险高五倍非黑人妇女。尽管已经开发了临床产后出血风险预测工具，但它们未能确定多达40％的病例；结果，目前没有在临床中广泛采用基于证据的预测工具实践。因此，紧急的有效，精确和个性化的产后出血风险预测工具是需要。最近，机器学习方法越来越多地用于发展准确的预测与传统的统计方法相比，具有出色性能的模型并发现新的模型预测因素，几乎没有事先预定。此外，可解释的机器学习方法允许透明决策和偏见的减少。这样，机器学习模型可能会导致更多与当前现有的工具相比，准确的产后出血预测。此外，由于多达18％产后出血风险是家族性的，许多临床风险因素与产后有关出血具有良好的多基因结构，使用多基因风险工具可能会进一步增强产后出血风险预测。根据NIH提高计划的目标，以提高孕产妇的安全性结果，我们在这里提议开发一种高保真算法，结合了临床和遗传因素，更准确地预测孕妇产后出血的风险。我们将利用我们的富人患者数据库和最先进的计算工具：（1）开发改进的算法以对患者进行分层产后出血风险，重点是透明度和降低偏见，（2）描述贡献产后出血风险的遗传学。总体而言，该项目将提高我们精确预测的能力有产后出血风险的患者，并研究了新的预测因子，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的新颖应用结果。最终，我们旨在在临床实践中验证和实施这些工具，从而极大地增强了预防母体发病率和死亡率的能力。通过完成这些目标，我将发展一个特定技能集对于建立我的研究轨迹和向独立过渡至独立性至关重要的特定技能集 - 科学家利用翻译计算方法来预测和改善不良产科结果。