A joint model for lung function and nutritional status decline with recurrent pulmonary exacerbations, death, and lung transplantation using cystic fibrosis patient Registry data

Objectives: CF primarily affects the lungs and digestive system. Direct associations between recurrent acute respiratory events, known as pulmonary exacerbations (PEs), and markers of lung function and nutritional decline have been reported, but have been limited to continuous longitudinal markers of lung function and nutrition with time-to-first PE, thus neglecting subsequent occurrences. This limitation is primarily caused by a lack of appropriate and robust statistical software, which may hinder CF epidemiologic studies of PE recurrence.Methods: For this retrospective cohort study of the U.S. CF Foundation Patient Registry, we jointly model the association between lung function decline (FEV 1) and evolution of growth and nutritional status (BMI) with the risk of recurrent PE, and the risk of lung transplant or death by using all available data. We employ a novel approach to investigate how the underlying value and slope of FEV1 and BMI trajectories associate with mortality/transplantation and PE recurrence. We accommodate non-risk periods during care episodes and risk periods defined by the gap or calendar timescale. The developed joint model for multiple longitudinal outcomes, recurrent and terminal events is available in the R package JMbayes2.Results: Preliminary results suggest that FEV 1 and BMI are associated with risks of experiencing either death/transplantation or PE. The model estimates an association of −0.02 (95%CI −0.03, −0.01) and 0.01 (95%CI −0.02, 0.04) between the risk of experiencing a PE and FEV1 or BMI, respectively.Conclusion: Incorporating all recurrent events with multiple markers to represent lung function decline and nutritional status enhances our understanding of risks posed by PEs. Our implementation enables a more efficient use of all available Registry data. Therefore, it brings new insights into CF disease progression and contributes to precise monitoring and comprehensive treatment strategies

Investigating the relationship between lung function decline and time to death or lung transplantation, accounting for geographical variability

Objectives: FEV1 % predicted is commonly used to monitor lung function decline and to predict mortality and transplantation in the CF population. An association between CF patients’ lung function decline and survival or lung transplantation has been previously reported, however, it is unknown how geographical location may affect this relationship.Methods: We conducted a longitudinal cohort study of patients receiving care at a CF centre in the midwestern United States, acquiring demographic and clinical characteristics. Prior joint models applied to CF natural history cohorts indicate accelerated lung function decline increases the hazard of lung transplant or death. However, previous analysis has ignored the spatial variability among CF patients living in different regions. To explore how this association changes between different geographical areas, we first clustered zip-codes by deprivation index and included this information as a fixed effect interacting with the longitudinal outcome in the survival sub-model. Posteriorly, we extended the standard joint model to incorporate a spatial frailty effect in the survival sub-model to account for unobserved heterogeneity among individuals residing in the city location of the care centre.Results: Preliminary results assuming 203 patients with 10,920 observations revealed that the association between FEV1 decline with survival varies across regions. The association varied from −0.14 (95%CI −0.32, 0.03) in regions with large deprivation index to −0.22 (95%CI −0.4, −0.05) in regions with small deprivation index. These results suggest that the spatial risk of death or transplantation varied across different regions. Conclusion: Better understanding how the association between lung function decline and death or lung transplantation changes across different geographical regions could bring new insights into CF disease progression, thereby paving the way towards more personalised monitoring and treatment strategies locally

43: Monitoring and phenotyping rapid cystic fibrosis disease progression using community characteristics and environmental exposures

Background: The extent to which rapid CF disease progression is predicted by community characteristics and environmental exposures (geomarkers) is unknown. We sought to predict and phenotype rapid lung function decline using individual-level geomarkers.Methods: We conducted a longitudinal cohort study (N = 33,972, ≥6 years old) of the CF Foundation Patient Registry (2003–2017). Geomarkers were ambient air pollution concentrations and hazard indices from the Environmental Protection Agency’s Environmental Justice Screening and Mapping Tool; land usage information from the Multi-Resolution Land Characteristics Consortium; indices of community material deprivation and crime, each linked to 5-digit zip codes. Novel longitudinal modeling with penalized variable selection was used to predict FEV1 decline with demographic/clinical characteristics and novel geomarkers as covariates. Covariate adjusted sparse functional principal component analysis was used to cluster pediatric patient-level FEV1 trajectories (aged 6–21). The first principal component score from multivariate geomarkers served as a covariate.Results: In the overall population, established demographic/clinical predictors of rapid FEV1 decline were selected, including smoking (by the individual or exposure in primary residence). Modulator use corresponded to less decline. Selected geomarker-based risk factors included elevated exposure to PM2.5 and diesel particulate matter, total crime, and deprivation indices. Pediatric phenotypes of rapid decline corresponded to early, middle, and late timing of rapid decline. The first principal component from geomarker analysis had strong positive loadings for diesel particulate matter, air toxics respiratory hazard index, traffic proximity and volume, extent of impervious space, and a strong negative loading for extent of greenspace, representing a proxy of negative environment exposure. Early rapid decliners resided in areas with higher crime index (mean ± SD) were (89.3 ± 57.5), followed by middle (86.4 ± 59.8), then late (83.8 ± 59.7) decliners (all comparisons P < 0.05). Having rapid decline earlier associated with living near longer secondary roadways (early: 63593 ± 78297, middle: 59581 ± 73941, late: 55735 ± 68208; all comparisons P < 0.05). Elevated negative environmental exposure level linked to increased FEV1 trajectories in younger ages but decreased in older ages.Conclusion: Accounting for neighborhood total crime, deprivation, and air pollution improves accuracy to predict rapid decline. Pediatric patients with an earlier rapid decline phenotype are more likely to reside in areas with more air pollution, more impervious spaces, increased crime, less tree cover, and limited greenspace. Negative environmental exposure more severely affects lung function during late adolescence/early adulthood compared to early childhood

Abstracts from the 3rd Conference on Aneuploidy and Cancer: Clinical and Experimental Aspects

Published versio

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.�This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.�Steps required for mediation analyses in the seasonality model are shown.�The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions