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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.

美国每年出生的6万名极低出生体重（VLBW; <1500g）婴儿的护理和结局