Measuring Neonatal Regionalization

测量新生儿区域化

• 批准号: 10668862
• 负责人: Jochen Profit
• 金额: $ 74.53万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668862
• 关键词: Address; Administrator; Adult; Back; California; Caring; Characteristics; Chronic lung disease; Clinical; Data; Data Set; Data Sources; Dimensions; Effectiveness; Equity; Ethnic Origin; Ethnic Population; Evolution; Family; Funding; Geographic Factor; Geography; Gestational Age; Goals; Guidelines; Health; Health Services; Health system; Hospital Administrators; Hospitals; Individual; Inequity; Infant; Infant Care; Institution; Insurance; Insurance Carriers; Joints; Lead; Length of Stay; Link; Machine Learning; Maps; Mathematics; Measures; Methodology; Methods; Modeling; Morbidity - disease rate; Necrotizing Enterocolitis; Neonatal; Neonatal Intensive Care Units; Observational Study; Outcome; Output; Pathway Analysis; Patient Care; Patient Discharge; Patients; Pattern; Patterns of Care; Performance; Perinatal; Perinatal Care; Policy Maker; Population; Positioning Attribute; Premature Infant; Provider; Quality of Care; Race; Recommendation; Records; Research; Research Infrastructure; Research Personnel; Resources; Retinopathy of Prematurity; Risk; Sampling; Science; Services; Shapes; Structural Racism; Structure; System; Techniques; Testing; Time; United States; United States National Institutes of Health; Variant; Very Low Birth Weight Infant; Visual; Visualization; Work; adverse outcome; analytical tool; care delivery; care outcomes; clinical care; density; design; experience; financial incentive; graph theory; health equity; high risk infant; improved; innovation; intraventricular hemorrhage; machine learning algorithm; mortality; neonatal death; neonatal outcome; novel; population based; postnatal; primary outcome; secondary outcome; social; social factors; tool

Care and outcomes for the 60,000 very low-birth-weight (VLBW; <1500g) infants born annually in the United States varies widely. National guidelines recommend that care be organized along hierarchical regionalized care delivery networks, but too often these vulnerable infants are born in hospitals whose capabilities don't match patient need. This necessitates postnatal transfer which has been associated with excess morbidity and mortality. To date, research on regional care networks has been thwarted by a lack of appropriate linked data sets and mathematical tools to understand care network characteristics and their effect on neonatal outcomes. We propose to bridge this gap and advance health outcomes science by gaining a deep understanding of network characteristics and their links to clinical care and outcomes. We will accomplish this by using linked data sets, not available elsewhere, that allow for analysis of the individual and joint contributions of multi-level factors, including network factors on clinical outcomes. In addition, we will apply network analysis, a branch of graphical mathematics to visually display and quantify regionalized care network characteristics. We propose a large, near population-scale, observational study to analyze routinely collected data from 2010 to 2020 from >290,000 VLBW infants (>50% of all VLBW infants in the United States) in ~520 NICUs using linked vital records and patient discharge data from 17 states. This study is designed to achieve 3 specific aims: 1) Quantify regionalization and structure of transfer networks for VLBW infants across the United States; 2) Test the association of network structure with clinical quality of care and outcomes; and 3) Model optimized structure of perinatal transfers networks. Our analyses will employ network analysis as an innovative tool to measure care regionalization focusing on a high impact primary outcome (survival without major morbidity), as a substantive departure from prior work. Machine learning will be used to provide information on optimal network structures in terms of effectiveness, equity and efficiency. These models will reveal how networks would need to be modified to satisfy optimization goals and reveal potential trade-offs. We have a long track record of impactful research funded by the National Institute of Health using this data. We also have an opportunity to investigate more granular questions in California (140 NICUs), which has unique existing linkages to maternal and infant clinical and transport data. We expect our research to have an immediate positive impact because it is designed to result in actionable information for policy makers, administrators and clinicians to improve perinatal care delivery and equity.

美国每年出生的 60,000 名极低出生体重 (VLBW；<1500g) 婴儿的护理和结果 各州差异很大。国家指南建议按照分层区域化护理组织护理 分娩网络，但这些脆弱的婴儿往往出生在能力不匹配的医院 患者需要。这需要产后转移，这与过高的发病率和 死亡。迄今为止，由于缺乏适当的关联数据，对区域护理网络的研究受到阻碍 集和数学工具来了解护理网络特征及其对新生儿结局的影响。 我们建议通过深入了解来弥合这一差距并推进健康结果科学 网络特征及其与临床护理和结果的联系。我们将通过使用链接数据来实现这一点 其他地方没有的集合，可以分析多层次因素的单独和联合贡献， 包括影响临床结果的网络因素。此外，我们将应用网络分析，这是图形学的一个分支 数学来直观地显示和量化区域化护理网络特征。我们建议建造一个大型的、近的 人口规模的观察性研究，用于分析 2010 年至 2020 年从超过 290,000 名 VLBW 定期收集的数据 使用链接的生命记录和患者在约 520 个 NICU 中治疗婴儿（占美国所有 VLBW 婴儿的 50% 以上） 来自 17 个州的排放数据。本研究旨在实现 3 个具体目标： 1) 量化美国各地极低出生体重婴儿转移网络的区域化和结构； 2）测试网络结构与临床护理质量和结果的关联；和 3）对围产期转移网络的优化结构进行建模。 我们的分析将采用网络分析作为一种创新工具来衡量护理区域化，重点关注 高影响力的主要结果（无重大发病情况的生存），与之前的工作有实质性的不同。 机器学习将用于提供有关最佳网络结构有效性的信息， 公平和效率。这些模型将揭示网络需要如何修改才能满足优化的要求 目标并揭示潜在的权衡。我们拥有由国家资助的有影响力的研究的长期记录 卫生研究所使用此数据。我们还有机会调查更具体的问题 加利福尼亚州（140 个新生儿重症监护室），与母婴临床和运输数据有着独特的现有联系。我们 期望我们的研究能够产生立竿见影的积极影响，因为它旨在产生可操作的结果 为政策制定者、管理人员和临床医生提供改善围产期护理服务和公平性的信息。