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

早产相关通气控制：领导数据和协调中心 (LDCC)

• 批准号: 10004706
• 负责人: DOUGLAS E LAKE
• 金额: $ 77.89万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10004706
• 关键词: Acute; Algorithmic Software; Algorithms; Apnea; Arts; Big Data; Biological Markers; Bradycardia; Breathing; Bronchopulmonary Dysplasia; Budgets; Characteristics; Chronic; Chronic lung disease; Clinical; Clinical Research; Clinical Trials; Computer Models; Computers; Computing Methodologies; DNA; Data; Data Collection; Data Coordinating Center; Data Security; Data Set; Detection; Early Diagnosis; Event; Funding; Future; Goals; Health; Heart Rate; High Performance Computing; Home environment; Individual; Infant; Investigation; Laboratories; Leadership; Letters; Lung diseases; Maintenance; Medical; Mission; Modeling; Monitor; Morbidity - disease rate; Multicenter Studies; Necrotizing Enterocolitis; Neonatal Intensive Care Units; Outcome; Outcome Measure; Pathogenesis; Pattern; Performance; Phenotype; Physiological; Premature Infant; Prevention; Preventive measure; Preventive treatment; Protocols documentation; Public Health; Records; Reporting; Research; Resources; Respiration; Secure; Sepsis; Site; Statistical Data Interpretation; Statistical Models; Structure; Techniques; Testing; Time; Tissues; U-Series Cooperative Agreements; United States; United States National Institutes of Health; Universities; Virginia; Work; biobank; clinical database; clinically significant; cluster computing; cohort; computerized tools; cost; data exchange; data management; data privacy; data resource; experience; improved; improved outcome; innovation; mathematical model; novel; predictive modeling; premature; prevent; programs; prospective; research facility; respiratory; respiratory morbidity; response; signal processing; tool

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.

项目概要/摘要 预防早产儿慢性肺病的根本差距包括缺乏 理解进化控制的成熟允许维持 充分的氧合，以及不成熟的呼吸表型如何影响结果。实现 有效预防措施和治疗试验的长期目标包括检测和 分析大型临床信息数据库中的不成熟呼吸模式， 来自多个新生儿ICU的心肺监测数据，包括生命体征和波形。 该提案的目标是（1）自动化的、有效的检测不成熟的呼吸模式 由临床医生和数学家组成的团队，以及（2）领导和数据协调中心 （LDCC）的合作协议，以研究一个前瞻性的观察队列。中央 假设未成熟呼吸的量化将识别生理生物标志物， 作为预防和治疗的目标，以改善结果。拟定的多中心方案 目标1和2是开发未成熟呼吸的预测模型，并将其与临床相关 重要的呼吸结果。拟议的LDCC建立在该大学的经验， 成功完成心率特征监测试验，早产儿中最大的RCT 婴儿，NIH资助，并按时完成和预算。计算要求将通过以下方式得到满足： 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音乐会 实验室和英特尔公司。我们将在我们的生物储存库和组织中分离和储存DNA 研究机构，并与我们的临床试验办公室管理网站。大规模计算集群 专门用于这项工作的是日常使用。这些贡献预计将是（1）计算工具 用于预测呼吸结果，和（2）数据管理中的有效LDCC性能， 计算建模、生物储存库和临床研究管理。拟议的研究将 重要的是，这是更好的治疗和预防措施计划的第一步， 早产儿慢性肺病。拟议的监测数据高级分析是 创新，因为先进的计算和数据安全解决方案， 也为其他NIH多中心大数据研究提供了信息。