SCH: Prediction of Preterm Birth in Nulliparous Women

SCH：未产妇早产的预测

• 批准号: 9928205
• 负责人: Alexander M Friedman
• 金额: $ 25.91万
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9928205
• 关键词: Address; Algorithms; Behavioral; Caring; Clinical; Clinical Data; Data; Data Collection; Data Set; Decision Making; Diagnostic tests; Effectiveness; Emotional; Etiology; Family; Female; Future; Genes; Genetic; Geography; Hospitals; Individual; Infant; Instruction; Intervention; Journals; Knowledge; Learning; Literature; Machine Learning; 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; 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; 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）是一个重大的长期公共卫生问题，是导致死亡和死亡的主要原因。 新生儿长期残疾，给家庭和家庭带来严重的情感和经济后果 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 由于缺乏既往怀孕史的母亲（未生育过的妇女）。迄今为止大多数研究都 通过单变量分析，检查个体风险因素，遗传、环境或行为。 与 PTB 的关联，包括 GWAS 识别出六个基因中常见变异的适度贡献 地区。改进 PTB 预测的挑战是由于其多因素的固有复杂性 病因学以及缺乏能够整合和解释大型多学科数据的方法。我们的 之前的工作 [NSF Eager 1454855, 1454814] 开发了基于非遗传的 PTB 预测模型 母性属性。一个重要的问题是了解是否可以使用 PTB 病史以外的因素 识别处于危险中的未产妇患者。我们计划为 PTB 设计纵向风险预测方法 整合每一个可用数据。我们将解决当前文献中的三个重要空白 三个项目目标： 对未生育妇女及其患 PTB 的风险进行重点研究；结合遗传 因素与其他临床因素一起确定风险；并使用纵向数据和模型进行优化 安排患者就诊、检测和治疗。我们将重点关注最近发布的 NIH-NICHD 数据集 称为 nuMoM2b，这是一项针对种族/民族/地理多样化的前瞻性队列研究 人口10,038 未生育的单胎妊娠妇女。 我们的目标如下：（1）纵向早产预测； (2)临床与遗传相结合 风险预测特征； (3)评估方法在临床实践中的有效性。 相关性（参见说明）。 每年花费超过 260 亿美元用于 12% 出生婴儿的分娩和护理 在美国早产。一个关键的挑战是确定哪些女性早期患病风险最高 早产并制定干预措施。同样重要的是能够识别女性 风险最低，避免不必要且成本高昂的干预。我们的项目有潜力推进 关于这一长期存在的公共卫生问题的知识。