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

项目摘要/摘要 进行性肺部疾病是囊性纤维化患者死亡的主要原因。迅速衰落， 以肺功能加速丧失为特征，在CF患者中很常见，无法解释或 仅通过CFTR/基因功能障碍来预测。绘制环境暴露和社区的地图 预测特定患者的快速下降并为早期检测提供工具的特征(地理标记) 和中心级别的监测对于改变CF临床护理的精确度至关重要，并提供 有机会调整干预措施，以防止不可逆转的肺损伤。将这些工具转换为 继续使用过时的统计方法进一步阻碍了实践，这些方法忽视了 慢性阻塞性肺疾病临床病程中的非线性肺功能和反复肺加重，忽略已知 死亡率偏差可能导致对快速下降的不准确预测，并且没有利用现有的地理位置 关于地理标志的数据，如空气质量或附近的社会经济状况，以改善对 迅速衰落。在本提案中，我们将使用全面的地理编码算法、新的统计方法 和强大的计算医学工具，集成到临床算法中进行检测，以推动早期 干预肺部疾病的快速进展。本研究的总体目标是利用丰富的现金流转 登记册、现有的国家和地方环境数据来源以及预期收集的研究数据 准确预测发病个体快速下降的患者，并制定出可行的医疗-- 积极影响临床护理点决策的监控工具。我们最重要的假设是 交互式计算医学工具用于快速肺活量的动态预测和临床监测 CF值的下降将加强当地的疾病监测。这将通过将两者结合在一起来实现 慢性萎缩性胃炎患者已建立和新的环境暴露和社区特征。我们的特定 目标是1)早期、快速地经历肺功能衰退的表型患者 暴露；2)转换动态预测慢性阻塞性肺疾病患者肺功能快速下降和恶化 通过高维、多层次的联合模型映射环境因素；3)设计与实现 决策支持功能，监测实时肺功能下降和病情恶化的风险 个性化、特定于中心的CF患者管理。一旦系统能够准确和准确地快速预测 减少了，更好的前瞻性治疗决定将成为可能，从而产生更好的患者 结果和改进的精准医疗/护理。

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.