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 了解成熟的通气控制机制可以维持 充足的氧合，以及不成熟的呼吸表型如何影响结果。实现 试验有效预防措施和治疗的长期目标包括检测和治疗 对大型临床信息数据库中不成熟的呼吸模式进行分析 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) 对不成熟的呼吸模式进行自动化、经过验证的检测 由临床医生和数学家团队组成，以及 (2) 领导力和数据协调中心 （LDCC）感谢 NIH 的这项合作协议来研究前瞻性观察队列。中央 假设是，对不成熟呼吸的量化将识别可以识别的生理生物标志物 作为改善结果的预防和治疗目标。拟议的多中心协议 目标 1 和 2 开发不成熟呼吸的预测模型，并将其与临床联系起来 显着的呼吸结果。拟议的 LDCC 建立在该大学在 成功完成早产儿最大的心率特征监测试验 婴儿，由 NIH 资助并按时按预算完成。计算要求将满足 与我们的合作伙伴劳伦斯利弗莫尔国家大学合作建立新的弗吉尼亚大学中心 实验室和英特尔公司。我们将在我们的生物样本库和组织中分离并储存 DNA 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都是日常使用的。预计贡献为（1）计算工具 用于预测呼吸结果，以及 (2) LDCC 在数据管理方面的有效表现， 计算模型、生物样本库和临床研究管理。拟议的研究将 意义重大，因为这是更好的治疗和预防措施计划的第一步 早产儿慢性肺部疾病。建议对监测数据进行高级分析 创新是因为先进计算和数据安全的尖端解决方案可能 还为其他 NIH 多中心大数据研究提供信息。