Prematurity-Related Ventilatory Control: Leadership Data and Coordination Center (LDCC)

项目摘要/摘要 在预防早产儿慢性肺部疾病方面的基本差距包括缺乏 了解成熟的呼吸控制使维持呼吸功能的机制 充足的氧气，以及不成熟的呼吸表型如何对结果做出贡献。实现 有效预防措施和治疗试验的长期目标包括检测和治疗 在大型临床信息和数据库中分析不成熟的呼吸模式 来自多个新生儿ICU的心肺监测数据，包括生命体征和波形。 该方案的目标是(1)自动、有效地检测未成熟的呼吸模式 由临床医生和数学家团队以及(2)领导力和数据协调中心 (LDCC)签署了这项NIH合作协议，以研究一个前瞻性观察队列。中环 假设对未成熟呼吸的量化将识别出能够 作为改善结果的预防和治疗的目标。建议的多中心协议 HAS的目标1和2是开发未成熟呼吸的预测模型，并将它们与临床联系起来 严重的呼吸后果。拟议的区域发展合作中心建立在该大学#年的经验基础上。 成功完成心率特征监测试验，这是早产儿最大的随机对照试验 婴儿，由美国国立卫生研究院资助，按时并按预算完成。将通过以下方式满足计算要求 一个新的弗吉尼亚大学中心，并与我们的合作伙伴劳伦斯·利弗莫尔国家大学合作 实验室和英特尔公司。我们将分离DNA并将其存储在我们的生物库和组织中 研究设施，并与我们的临床试验办公室一起管理网站。大规模计算集群 专门用于这项工作的都是日常使用的。预计这些贡献将是：(1)计算工具 为了预测呼吸结果，以及(2)在数据管理方面的有效LDCC性能， 计算建模、生物信息库和临床研究管理。拟议的研究将 意义重大，因为这是更好的治疗方案和预防措施的第一步 早产儿的慢性肺部疾病。建议对监测数据进行高级分析 创新，因为先进计算和数据安全的尖端解决方案可能 还可以向NIH其他多中心的大数据研究提供信息。

Project summary/abstract Fundamental gaps in prevention of chronic lung disease in premature infants include the lack of understanding of mechanisms by which maturation of ventilatory control allows maintenance of adequate oxygenation, and how immature breathing phenotypes contribute to outcomes. Achieving the long-term goal of trials of effective preventive measures and treatments includes detection and analysis of immature breathing patterns in a large database of clinical information and cardiorespiratory monitoring data from multiple Neonatal ICUs, including vital signs and waveforms. The objectives of this proposal are (1) automated, validated detection of immature breathing patterns by teams of clinicians and mathematicians, and (2) a Leadership and Data Coordination Center (LDCC) for this NIH cooperative agreement to study a prospective observational cohort. The central hypothesis is that quantification of immature breathing will identify physiological biomarkers that can serve as targets for prevention and treatment that improve outcomes. A proposed multicenter protocol has Aims 1 and 2 to develop predictive models for immature breathing, and to relate them to clinically significant respiratory outcomes. The proposed LDCC builds on the experience of this university in successful completion of the heart rate characteristics monitoring trial, the largest RCT in premature infants, NIH-funded and completed on time and on budget. The computing requirements will be met by a new University of Virginia Center and in concert with our partners Lawrence Livermore National Laboratory and Intel Corporation. We will isolate and store DNA in our Biorepository and Tissue Research Facility, and manage sites with our Clinical Trials Office. Large-scale computing clusters dedicated for this work are in daily use. The contributions are expected to be (1) computational tools for prediction of respiratory outcomes, and (2) effective LDCC performance in data management, computational modeling, biorepository, and clinical studies management. The proposed research will be significant because it is the first step in programs for better therapies and preventive measures for chronic lung disease in premature infants. The proposed advanced analysis of monitoring data is innovative because of the cutting edge solutions to advanced computing and data security that may also inform other NIH multicenter studies of Big Data.