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

基本信息

批准号：
10579825
负责人：
Rhonda Szczesniak
金额：
$ 45.18万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
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/10579825
关键词：
Acceleration 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 Economic Conditions Environment Environmental Exposure Environmental Risk Factor Event Foundations Frequencies Functional disorder Genes Geography Individual Intervention Joints Link Localized Disease 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-Specific Antigen Provider Public Health Pulmonary Cystic Fibrosis Pulmonary Function Test/Forced Expiratory Volume 1 Recurrence Recurrent Malignant Neoplasm Registries Research Risk Science Socioeconomic Status Statistical Methods System Temperature Translating Translations Transplantation United States National Institutes of Health Variant Work 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 mortality non-genetic novel patient registry personalized care personalized decision point of care precision medicine predictive modeling prevent prognostic prospective pulmonary function pulmonary function decline rapid detection real time monitoring socioeconomics spatiotemporal support tools tool treatment strategy

项目摘要

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。

项目成果

期刊论文数量（35）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sweat metabolomics before and after intravenous antibiotics for pulmonary exacerbation in people with cystic fibrosis.

DOI：
10.1016/j.rmed.2021.106687
发表时间：
2022-01
期刊：
Respiratory medicine
影响因子：
4.3
作者：
Woodley FW;Gecili E;Szczesniak RD;Shrestha CL;Nemastil CJ;Kopp BT;Hayes D Jr
通讯作者：
Hayes D Jr

Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application.

DOI：
10.2196/23530
发表时间：
2020-12-16
期刊：
JMIR medical informatics
影响因子：
3.2
作者：
Wolfe C;Pestian T;Gecili E;Su W;Keogh RH;Pestian JP;Seid M;Diggle PJ;Ziady A;Clancy JP;Grossoehme DH;Szczesniak RD;Brokamp C
通讯作者：
Brokamp C

Bayesian regularization for a nonstationary Gaussian linear mixed effects model.

DOI：
10.1002/sim.9279
发表时间：
2022-02-20
期刊：
Statistics in medicine
影响因子：
2
作者：
Gecili E;Sivaganesan S;Asar O;Clancy JP;Ziady A;Szczesniak RD
通讯作者：
Szczesniak RD

Transparency and open access in CF research.

DOI：
10.1016/j.jcf.2020.03.007
发表时间：
2020-05
期刊：
Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
影响因子：
0
作者：
Stanojevic S;Szczesniak R
通讯作者：
Szczesniak R

Social-environmental phenotypes of rapid cystic fibrosis lung disease progression in adolescents and young adults living in the United States.

美国青少年和年轻人囊性纤维化肺病快速进展的社会环境表型。

DOI：
10.1016/j.envadv.2023.100449
发表时间：
2023
期刊：
Environmental advances
影响因子：
0
作者：
Palipana,AnushkaK;Vancil,Andrew;Gecili,Emrah;Rasnick,Erika;Ehrlich,Daniel;Pestian,Teresa;Andrinopoulou,Eleni-Rosalina;Afonso,PedroM;Keogh,RuthH;Ni,Yizhao;Dexheimer,JudithW;Clancy,JohnP;Ryan,Patrick;Brokamp,Cole;Szczesniak,
通讯作者：
Szczesniak,