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: 9337265
• 负责人: DOUGLAS E LAKE
• 金额: $ 77.95万
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9337265
• 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 Simulation; Computers; Computing Methodologies; DNA; Data; Data Collection; Data Coordinating Center; Data Security; Data Set; Databases; 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; Privacy; 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; clinically significant; cluster computing; cohort; computerized tools; cost; data management; data resource; experience; improved; improved outcome; innovation; mathematical model; novel; predictive modeling; premature; prevent; programs; prospective; research facility; respiratory; 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.

项目摘要/摘要 预防早产儿的慢性肺病的基本差距包括缺乏 了解通气控制成熟的机制可以维护的机制 充足的氧合以及未成熟的呼吸表型如何导致结果。实现 有效预防措施和治疗试验的长期目标包括检测和 在大型临床信息数据库中分析未成熟的呼吸模式和 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，经过验证的未成熟呼吸模式的检测 由临床医生和数学家团队以及（2）领导和数据协调中心 （LDCC）关于NIH合作协议，以研究前瞻性观察队列。中央 假设是，未成熟呼吸的量化将确定可以 作为预防和治疗的靶标，可以改善预后。提出的多中心协议 目标是1和2，以开发未成熟呼吸的预测模型，并将其与临床联系起来 明显的呼吸结果。拟议的LDCC建立在这所大学的经验基础上 成功完成心率特征监测试验，这是最大的RCT NIH资助的婴儿按时并按预算完成。计算要求将通过 弗吉尼亚大学的新大学中心，并与我们的合作伙伴劳伦斯·利弗莫尔国家 实验室和英特尔公司。我们将隔离并存储DNA在我们的生物座和组织中 研究设施，并通过我们的临床试验办公室管理站点。大规模计算集群 专门用于这项工作的日常使用。预计该贡献将为（1）计算工具 为了预测呼吸结局，以及（2）数据管理中有效的LDCC性能， 计算建模，生物验证和临床研究管理。拟议的研究将 要重要，因为这是更好疗法和预防措施的计划的第一步 早产婴儿的慢性肺病。提议的监视数据的高级分析是 创新性是因为具有先进计算和数据安全性的最先进解决方案 还告知其他NIH大数据多中心研究。