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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.

摘要