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

项目总结/摘要 进行性肺病是囊性纤维化（CF）患者死亡的主要原因。快速下降， 其特征是肺功能加速丧失，在CF患者中很常见，无法解释或 仅通过CFTR/基因功能障碍预测。绘制环境暴露和社区 预测患者特异性快速下降并提供早期检测工具的特征（地理标志物） 和监测在中心一级是必不可少的，以改变精确的CF临床护理，并提供了一个 有机会调整干预措施，以防止不可逆转的肺损伤。将这些工具转化为 继续使用过时的统计方法进一步阻碍了实践， 非线性肺功能和CF临床过程中的复发性肺加重，不考虑已知的 死亡率偏差可能导致对快速下降的不准确预测， 关于地理标志的数据，如空气质量或邻近的社会经济条件，以改善对 快速下降。在这个建议中，我们将利用全面的地理编码算法，新颖的统计方法， 和强大的计算医学工具，用于集成到临床算法中，以便及早进行检测 快速肺部疾病进展的干预。本研究的总体目标是利用丰富的CF 登记册、现有的国家和地方环境数据来源以及前瞻性收集的研究数据， 准确预测发病迅速下降的个别病人，并制定一个可行的医疗- 监测工具，积极影响CF床旁决策。我们的首要假设是 用于快速肺部感染的动态预测和临床监测的交互式计算医学工具 CF的下降将加强当地疾病监测。这将通过合并两个 CF患者的既定和新的环境暴露和社区特征。我们的具体 目的是1）表型患者谁经历早期，快速肺下降告知环境 暴露; 2）转换动态预测CF患者的肺功能快速下降和恶化 通过与环境因素的高维、多层次联合模型映射; 3）设计并实现 决策支持能力，监测实时肺功能下降和恶化的风险， 个性化、中心特定的CF患者管理。一旦系统能够准确、精确地预测 下降到位，更好的前瞻性治疗决策将成为可能，从而更好的患者 改善精准医疗/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.