Abstract
BackgroundEpidemiologic surveillance of lung function is key to clinical care of individuals with cystic fibrosis, but lung function decline is nonlinear and often impacted by acute respiratory events known as pulmonary exacerbations. Statistical models are needed to simultaneously estimate lung function decline while providing risk estimates for the onset of pulmonary exacerbations, in order to identify relevant predictors of declining lung function and understand how these associations could be used to predict the onset of pulmonary exacerbations.MethodsUsing longitudinal lung function (FEV1) measurements and time-to-event data on pulmonary exacerbations from individuals in the United States Cystic Fibrosis Registry, we implemented a flexible semiparametric joint model consisting of a mixed-effects submodel with regression splines to fit repeated FEV1 measurements and a time-to-event submodel for possibly censored data on pulmonary exacerbations. We contrasted this approach with methods currently used in epidemiological studies and highlight clinical implications.ResultsThe semiparametric joint model had the best fit of all models examined based on deviance information criterion. Higher starting FEV1 implied more rapid lung function decline in both separate and joint models; however, individualized risk estimates for pulmonary exacerbation differed depending upon model type. Based on shared parameter estimates from the joint model, which accounts for the nonlinear FEV1 trajectory, patients with more positive rates of change were less likely to experience a pulmonary exacerbation (HR per one standard deviation increase in FEV1 rate of change = 0.566, 95% CI 0.516–0.619), and having higher absolute FEV1 also corresponded to lower risk of having a pulmonary exacerbation (HR per one standard deviation increase in FEV1 = 0.856, 95% CI 0.781–0.937). At the population level, both submodels indicated significant effects of birth cohort, socioeconomic status and respiratory infections on FEV1 decline, as well as significant effects of gender, socioeconomic status and birth cohort on pulmonary exacerbation risk.ConclusionsThrough a flexible joint-modeling approach, we provide a means to simultaneously estimate lung function trajectories and the risk of pulmonary exacerbations for individual patients; we demonstrate how this approach offers additional insights into the clinical course of cystic fibrosis that were not possible using conventional approaches.
Highlights
Epidemiologic surveillance of lung function is key to clinical care of individuals with cystic fibrosis, but lung function decline is nonlinear and often impacted by acute respiratory events known as pulmonary exacerbations
Lung function is measured primarily as forced expiratory volume in 1 s of percent predicted, which is the primary marker of disease severity in individuals with cystic fibrosis (CF). FEV1 declines in a nonlinear fashion over age and exhibits substantial heterogeneity both between patients and within an individual patient over time (Fig. 1)
Covariates of interest were selected from the literature on FEV1 [13, 14] and pulmonary exacerbation [5, 6] as separate models, and included age, gender, initial FEV1 measure and baseline lower socioeconomic status; a birth cohort covariate was used to adjust for potential left truncation bias; time-varying covariates included CF-related diabetes (CFRD, with or without fasting hyperglycemia), positive cultures for methicillin-resistant staphylococcus aureus (MRSA), Burkholderia cepacia (B. cepacia), and Pseudomonas aeruginosa (Pa)
Summary
Epidemiologic surveillance of lung function is key to clinical care of individuals with cystic fibrosis, but lung function decline is nonlinear and often impacted by acute respiratory events known as pulmonary exacerbations. CF epidemiologic studies have not accounted for nonlinearity in the FEV1 trajectory; recent approaches have included piecewise polynomials, either in the form of regression splines or change-point models, to estimate decline in FEV1 [2, 3]. These approaches have shown that F EV1 decline is variable with maximal loss occurring in adolescence and early adulthood
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
