Measuring Neonatal Regionalization

美国每年出生的6万名极低出生体重儿的护理和结局 各州的情况差异很大。国家指南建议按照分级的区域化护理来组织护理 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 耐心的需要。这就需要出生后的移植，这与过度的发病率和 死亡率。到目前为止，关于区域护理网络的研究因缺乏适当的关联数据而受阻 套装和数学工具，以了解护理网络特征及其对新生儿结局的影响。 我们建议弥合这一差距，并通过深入了解 网络特征及其与临床护理和结果的联系。我们将使用关联数据来实现这一点 其他地方没有的集合，允许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，我们还将应用网络分析，这是图形学的一个分支 直观地显示和量化区域化护理网络特征的数学方法。我们建议建立一个大型的、近乎 人口规模的观察性研究，以分析从2010年至2020年从29万名VLBW；常规收集的数据 使用相关生命记录和患者的约520个NICU中的婴儿(占美国所有VLBW婴儿的50%) 来自17个州的放电数据。这项研究旨在实现三个具体目标： 1)量化全美极低出生体重儿转运网络的区划和结构； 2)测试网络结构与临床护理质量和结果的关系；以及 3)建立优化的围产期转运网络结构模型。 我们的分析将使用网络分析作为一种创新工具来衡量护理区域化，重点是 高影响的主要结果(存活而没有重大发病率)，作为与先前工作的实质性背离。 机器学习将用于提供关于效率方面的最佳网络结构的信息， 公平和效率。这些模型将揭示如何修改网络才能满足优化要求 目标，并揭示潜在的权衡。我们在国家资助的有影响力的研究方面有很长的记录 卫生研究所使用这一数据。我们还有机会调查更多细粒度的问题 加州(140个NICU)，与母婴临床和运输数据有独特的现有联系。我们 预计我们的研究将立即产生积极影响，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的信息，以改善围产期护理的提供和公平。

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.