SCH: Prediction of Preterm Birth in Nulliparous Women

SCH：未产妇早产的预测

• 批准号: 10459433
• 负责人: Alexander M Friedman
• 金额: $ 24.11万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459433
• 关键词: Address; Algorithms; Behavioral; Caring; Clinical; Clinical Data; Data; Data Collection; Data Set; Decision Making; Diagnostic tests; Emotional; Etiology; Family; Female; Future; Genes; Genetic; Geography; Hospitals; Individual; Infant; Instruction; Intervention; Journals; Knowledge; Learning; Literature; Medical; Medical Genetics; Methods; Modeling; Mothers; National Institute of Child Health and Human Development; New York; Nulliparity; Paper; Patient Schedules; Patients; Pattern; Phenotype; Population Heterogeneity; Pregnancy; Pregnancy Histories; Premature Birth; Presbyterian Church; Prospective cohort study; Public Health; Recording of previous events; Research; Risk; Risk Factors; Series; Social Impacts; Societies; Testing; Time; United States; United States National Institutes of Health; Variant; Visit; Woman; Work; base; clinical practice; design; disability; effectiveness evaluation; effectiveness testing; genetic association; genetic information; genome wide association study; graduate student; high risk; improved; insight; intervention cost; minority student; mortality; multidisciplinary; neonate; non-genetic; phenotypic data; predictive modeling; racial and ethnic; recruit; risk prediction; support vector machine; symposium; undergraduate research

Preterm Birth (PTB) is a major long-lasting public health problem being the leading cause of mortality and long-term disabilities among neonates, with heavy emotional and financial consequences to families and society. Prediction of PTB risk has been an exceedingly challenging problem, in particular for first time mothers (nulliparous women) due to the lack of prior pregnancy history. Most studies to date have examined individual risk factors, genetic, environmental, or behavioral, through univariate analyses of their association with PTB, including GWAS identifying modest contribution of common variants across six gene regions. The challenge of improving PTB prediction is due to the inherent complexity of its multifactorial etiology and the lack of approaches capable of integrating and interpreting large multidisciplinary data. Our previous work [NSF Eager 1454855, 1454814] developed predictive models for PTB based on non-genetic maternal attributes. An important question is to know whether factors other than history of PTB can be used to identify a nullipara patient at risk. We plan on devising longitudinal risk prediction methods for PTB that integrate every piece of available data. We will address three important gaps in current literature as our three project objectives: a focused study of nulliparous women and their risk for PTB; combining genetic factors with other clinical factors to determine risk ; and using longitudinal data and models to optimize scheduling of patient visits, testing and treatment. We will focus on a recently released NIH-NICHD dataset called nuMoM2b, which is a prospective cohort study of a racially/ethnically/geographically diverse population of10 ,038 nulliparous women with singleton gestation . Our aims are as follows: (1) Longitudinal Preterm Birth Prediction ; (2) Combining clinical and genetic features for risk prediction ; (3) Assessing the effectiveness of the methods in clinical practice. RELEVANCE (See instructions) . Over 26 billion dollars are spent annually on the delivery and care of the 12% of infants who are born preterm in the United States. A crucial challenge is to identify women who are at the highest risk for early preterm birth and to develop interventions. Equally important, would be the ability to identify women at the lowest risk to avoid unnecessary and costly interventions. Our project has the potential to advance knowledge about this long-lasting public health problem.

早产（PTB）是一个主要长期持久的公共卫生问题，是死亡率和 新生儿之间的长期残疾，对家庭的情感和财务影响很大， 社会。 PTB风险的预测一直是一个极具挑战性的问题，尤其是第一次 由于缺乏先前的怀孕史，母亲（无效的妇女）。迄今为止的大多数研究都有 通过单变量分析，检查了遗传，环境或行为的个体风险因素 与PTB的关联，包括识别六个基因共同变异的适度贡献的GWA 地区。改善PTB预测的挑战是由于其多因素的固有复杂性 病因和缺乏能够整合和解释大型多学科数据的方法。我们的 以前的工作[NSF急切1454855，1454814]基于非遗传的PTB的预测模型 孕产妇属性。一个重要的问题是知道是否可以使用PTB历史以外的其他因素 确定有危险的无效患者。我们计划为PTB设计纵向风险预测方法 集成每个可用数据。我们将解决当前文献中的三个重要差距 三个项目目标：一项针对无妇女的重点研究及其PTB的风险；结合遗传 与其他临床因素确定风险的因素；并使用纵向数据和模型来优化 时间安排患者就诊，测试和治疗。我们将重点关注最近发布的NIH-NICHD数据集 称为numom2b，这是一项对种族/种族/地理上多样化的前瞻性队列研究 10，038个无数妇女有单身妊娠的妇女。 我们的目标如下：（1）纵向早产预测； （2）结合临床和遗传 风险预测的功能； （3）评估该方法在临床实践中的有效性。 相关性（请参阅说明）。 每年超过260亿美元用于出生12％的婴儿的交付和照顾 在美国的早产。一个至关重要的挑战是确定早期风险最高的妇女 早产并制定干预措施。同样重要的是，可以识别女性 避免不必要和昂贵的干预措施的最低风险。我们的项目有可能进步 关于这个持久的公共卫生问题的知识。