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; <1500 g）婴儿的护理和结局 各州差异很大。国家指导方针建议沿着分级区域化护理组织护理 但这些脆弱的婴儿往往出生在能力不匹配的医院 病人的需要。这就需要产后移植，而产后移植与高发病率有关， mortality.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一直受到阻碍 集合和数学工具，以了解护理网络的特点及其对新生儿结局的影响。 我们建议通过深入了解以下内容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。我们将通过使用关联数据来实现这一点 其他地方没有的成套资料，可以分析多层次因素的单独和联合作用， 包括临床结果的网络因素。此外，我们将应用网络分析，一个分支的图形 数学来直观地显示和量化区域化护理网络特征。我们提出了一个大的，近 人群规模的观察性研究，分析2010年至2020年从> 290，000 VLBW中常规收集的数据 使用链接的生命记录和患者，在约520个NICU中的婴儿（>美国所有VLBW婴儿的50%） 来自17个国家的数据。本研究旨在实现3个具体目标： 1)量化美国极低出生体重婴儿转运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;以及 3)围产期转运网络结构优化模型。 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，重点是 高影响力的主要结果（无重大发病率的生存率），与先前的工作有实质性的不同。 机器学习将用于提供关于最佳网络结构的信息， 公平和效率。这些模型将揭示网络需要如何修改以满足优化 目标和揭示潜在的权衡。我们有一个由国家资助的有影响力的研究的长期记录 卫生研究所使用这些数据。我们也有机会研究更细粒度的问题， 加州（140个NICU），该地区与母婴临床和运输数据有着独特的联系。我们 我希望我们的研究能立即产生积极的影响，因为它的目的是产生可操作的 为决策者、行政人员和临床医生提供信息，以改善围产期护理的提供和公平性。