Measuring Neonatal Regionalization

测量新生儿区域化

基本信息

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
项目类别：
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

项目摘要

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.

每年在曼联出生的60,000个非常低的生产重量（VLBW; <1500G）的婴儿的护理和结果状态变化很大。国家准则建议沿等级区域化护理组织护理送货网络，但是这些脆弱的婴儿经常出生在能力不匹配的医院患者需要。这需要产后转移，这与多余的发病率有关死亡。迄今为止，由于缺乏适当的链接数据，对区域护理网络的研究受到了阻碍集合和数学工具了解护理网络特征及其对新生儿结果的影响。我们建议通过深入了解网络特征及其与临床护理和结果的联系。我们将使用链接数据来实现这一目标在其他地方无法使用的集合可以分析多层次因素的个体和联合贡献，包括有关临床结果的网络因素。此外，我们将应用网络分析，图形分析数学以视觉显示和量化区域化护理网络特征。我们提出了一个大的，附近从> 290,000 VLBW，从2010年到2020年分析的人口尺度，观察性研究，分析了常规收集的数据使用链接的生命记录和患者，在〜520 NICUS中，婴儿（>美国所有VLBW婴儿的50％）来自17个州的排放数据。这项研究旨在实现3个具体目标： 1）量化美国VLBW婴儿的转移网络的区域化和结构； 2）测试网络结构与临床护理和结果的关联；和 3）模型优化围产期转移网络的结构。我们的分析将采用网络分析作为一种创新工具，以衡量关注的护理区域化高影响力的主要结果（没有重大发病率的生存），是与先前的工作相去甚远。机器学习将用于提供有关有效性的最佳网络结构的信息公平和效率。这些模型将揭示如何需要修改网络以满足优化目标并揭示潜在的权衡。我们拥有由国家资助的有影响力研究的悠久记录卫生研究院使用这些数据。我们也有机会调查更多详细问题加利福尼亚（140 NICUS），它与孕产妇和婴儿临床和运输数据具有独特的现有联系。我们期望我们的研究会产生直接的积极影响，因为它旨在实现可行的政策制定者，管理人员和临床医生的信息，以改善围产期护理和公平性。