R01- Mapping environmental contributions to rapid lung disease progression in cystic fibrosis

R01-绘制环境对囊性纤维化肺部疾病快速进展的影响

• 批准号: 10078975
• 负责人: Rhonda Szczesniak
• 金额: $ 47.05万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-18 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10078975
• 关键词: Accounting; Acute; Adolescence; Age; Air; Algorithms; Antibiotics; Big Data Methods; Caring; Cause of Death; Characteristics; Clinical; Clinical Management; Clinical assessments; Clinical effectiveness; Communities; Complex; Coupled; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Data; Data Analyses; Data Sources; Decision Making; Detection; Disease; Disease Progression; Drops; Early Diagnosis; Early Intervention; Environment; Environmental Exposure; Environmental Risk Factor; Event; Foundations; Frequencies; Functional disorder; Genes; Geography; Intervention; Joints; Lead; Light; Link; Lung; Lung diseases; Maps; Measures; Medical; Medicine; Mission; Modeling; Monitor; Neighborhoods; Outcome; Patient-Focused Outcomes; Patients; Phenotype; Positioning Attribute; Predictive Analytics; Predictive Factor; Prevention; Prognosis; Prostate; Prothrombin; Provider; Public Health; Pulmonary Function Test/Forced Expiratory Volume 1; Recurrence; Registries; Research; Risk; Science; Socioeconomic Status; Statistical Methods; System; Temperature; Time; Translating; Translations; Transplantation; United States National Institutes of Health; Variant; Work; base; cancer recurrence; cancer risk; clinical care; clinical decision-making; clinical encounter; clinical practice; cystic fibrosis patients; decision making algorithm; design; early experience; emerging adult; environmental intervention; epidemiological model; flexibility; high dimensionality; improved; individual patient; innovation; lung injury; male; man; mortality; non-genetic; novel; patient registry; personalized care; personalized decision; personalized predictions; point of care; precision medicine; predictive modeling; prevent; prognostic; prospective; pulmonary function; pulmonary function decline; real time monitoring; socioeconomics; spatiotemporal; support tools; tool; treatment strategy

PROJECT SUMMARY/ABSTRACT Progressive lung disease is the leading cause of death in individuals with cystic fibrosis (CF). Rapid decline, characterized by accelerated loss of lung function, is common for CF patients, and cannot be explained or predicted by CFTR/gene dysfunction alone. Mapping the environmental exposures and community characteristics (geomarkers) that predict patient-specific rapid decline and providing tools for earlier detection and monitoring at the center level are essential to transforming the precision of CF clinical care, and offer an opportunity to adjust interventions to prevent irreversible lung damage. The translation of these tools into practice is further hindered by continued use of antiquated statistical methods that ignore the interplay between nonlinear lung function and recurrent pulmonary exacerbations in the clinical course of CF, disregard known mortality biases that can lead to inaccurate projections of rapid decline, and do not leverage extant geographic data on geomarkers, such as air quality or neighborhood socioeconomic conditions, to improve prediction of rapid decline. In this proposal, we will utilize comprehensive geocoding algorithms, novel statistical methods and powerful computational medicine tools for integration into clinical algorithms for the detection to drive early intervention of rapid lung disease progression. The overall objective of this research is to leverage a rich CF registry, extant national and local environmental data sources and prospectively collected study data to accurately forecast the onset of rapid decline in individual patients, and to develop a feasible medical- monitoring tool that positively impacts CF point-of-care decision-making. Our overarching hypothesis is that interactive computational medicine tools for dynamic prediction and clinical surveillance of rapid pulmonary decline in CF will enhance local disease monitoring. This will be accomplished by incorporating both established and novel environmental exposures and community characteristics of CF patients. Our specific aims are to 1) phenotype patients who experience early, rapid pulmonary decline informed by environmental exposures; 2) transform dynamic prediction of rapid lung-function decline and exacerbations in CF patients through high-dimensional, multi-level joint model mapping with environmental factors; 3) design and implement decision support capabilities that monitor real-time lung-function decline and risk of exacerbations for personalized, center-specific CF patient management. Once systems to accurately and precisely predict rapid decline are in place, better prospective treatment decisions will become possible, resulting in better patient outcomes and improved precision medicine/care.

项目摘要/摘要 进行性肺部疾病是囊性纤维化（CF）个体死亡的主要原因。快速下降， CF患者的特征是肺功能加速丧失，无法解释或无法解释或 仅由CFTR/基因功能障碍预测。映射环境暴露和社区 预测患者特定快速下降并提供早期检测的工具的特征（地理标志物） 中心层的监视对于改变CF临床护理的精度至关重要，并提供 调整干预措施以防止不可逆的肺损伤的机会。这些工具转换为 继续使用过时的统计方法，进一步阻碍了练习 在CF的临床过程中，非线性肺功能和复发性肺部恶化，无视已知 死亡率偏见可能导致不准确的预测快速下降，并且不利用现有的地理位置 有关地震标志的数据，例如空气质量或邻里社会经济条件，以提高预测 快速下降。在此提案中，我们将利用全面的地理编码算法，新型的统计方法 和强大的计算医学工具，用于集成到临床算法中，以提早驾驶 快速肺部疾病进展的干预。这项研究的总体目的是利用丰富的CF 注册表，现存的国家和地方环境数据来源以及前瞻性收集的研究数据 准确地预测个别患者快速下降的开始，并发展可行的医学 - 监控工具对CF的即将到来的决策产生积极影响。我们的总体假设是 交互式计算医学工具，用于动态预测和临床监测快速肺部 CF的下降将增强当地疾病监测。这将通过两者结合来实现 CF患者的建立且新颖的环境暴露和社区特征。我们的具体 目的是1）表型患者，他们经历了早期，快速肺部下降的衰落。 暴露； 2）转化CF患者快速肺部功能下降和加剧的动态预测 通过具有环境因素的高维的多级关节模型映射； 3）设计和实施 监测实时肺功能下降和恶化风险的决策支持能力 个性化的，特定于中心的CF患者管理。一旦系统准确，准确地预测快速 下降已经到位，更好的前瞻性治疗决策将成为可能，从而使患者更好 结果和改进的精密药物/护理。