Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations.

Rationale: Studies estimating the rate of lung function decline in cystic fibrosis have been inconsistent regarding the methods used. How the methodology used impacts the validity of the results and comparability between studies is unknown. Objectives: The Cystic Fibrosis Foundation established a work group whose tasks were to examine the impact of differing approaches to estimating the rate of decline in lung function and to provide analysis guidelines. Methods: We used a natural history cohort of 35,252 individuals with cystic fibrosis aged ⩾6 years in the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified the rate of forced expiratory volume in 1 second (FEV1) decline (percent predicted per year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 yr, 2-5 yr, entire duration). Results: Rate of FEV1 decline estimates (percent predicted per year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios, except for short-term follow-up (both were ∼1.4). Rate of decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best, except for short-term follow-up (<2 yr). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted per year in FEV1 was associated with a 1.52-fold (52%) increase in the hazard of death/lung transplant, but the results exhibited immortal cohort bias. Conclusions: Differences were as high as 0.5% predicted per year between rate of decline estimates, but we found estimates were robust to lung function data availability scenarios, except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals.

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.

LIBRETTO-432, a phase III study of adjuvant selpercatinib or placebo in stage IB-IIIA RET fusion-positive non-small-cell lung cancer.

Selpercatinib, a first-in-class, highly selective and potent central nervous system-active RET kinase inhibitor demonstrated clinically meaningful activity with manageable toxicity in pretreated and treatment-naive advanced/metastatic RET fusion-positive non-small-cell lung cancer (NSCLC). LIBRETTO-432 is a global, randomized, double-blind, phase III trial evaluating selpercatinib versus placebo in stage IB-IIIA, RET fusion-positive NSCLC, previously treated with definitive surgery or radiation; participants must have undergone available anti-cancer therapy (including chemotherapy or durvalumab) or not be suitable for it, per investigator's discretion. The primary end point is investigator-assessed event-free survival (EFS) in the primary analysis population (stage II-IIIA RET fusion-positive NSCLC). Key secondary end points include EFS in the overall population, overall survival, and time to distant disease recurrence in the central nervous system.

Selpercatinib is approved in multiple countries for the treatment of advanced or metastatic RET-altered lung cancers. Selpercatinib has shown promising efficacy and safety results in patients with advanced/metastatic RET fusion-positive NSCLC. This is a summary of the LIBRETTO-432 study which compares selpercatinib with placebo in patients with earlier stages (stage IB-IIIA) of RET fusion-positive NSCLC, who have already undergone surgery or radiotherapy and applicable adjuvant chemotherapy. This study is active and currently recruiting new participants. This trial will evaluate how long people live without evidence of cancer recurrence, both during and after treatment. Side effects will also be evaluated in this study. Clinical Trial Registration: NCT04819100 (ClinicalTrials.gov).

An empirical comparison of segmented and stochastic linear mixed effects models to estimate rapid disease progression in longitudinal biomarker studies.

Longitudinal studies of rapid disease progression often rely on noisy biomarkers; the underlying longitudinal process naturally varies between subjects and within an individual subject over time; the process can have substantial memory in the form of within-subject correlation. Cystic fibrosis lung disease progression is measured by changes in a lung function marker (FEV1), such as a prolonged drop in lung function, clinically termed rapid decline. Choosing a longitudinal model that estimates rapid decline can be challenging, requiring covariate specifications to assess drug effect while balancing choices of covariance functions. Two classes of longitudinal models have recently been proposed: segmented and stochastic linear mixed effects (LMEs) models. With segmented LMEs, random changepoints are used to estimate the timing and degree of rapid decline, treating these points as structural breaks in the underlying longitudinal process. In contrast, stochastic LMEs, such as random walks, are locally linear but utilize continuously changing slopes, viewing bouts of rapid decline as localized, sharp changes. We compare commonly utilized variants of these approaches through an application using the Cystic Fibrosis Foundation Patient Registry. Changepoint modeling had the worst fit and predictive accuracy but certain covariance forms in stochastic LMEs produced problematic variance estimates.

An Animated Functional Data Analysis Interface to Cluster Rapid Lung Function Decline and Enhance Center-Level Care in Cystic Fibrosis.

Identifying disease progression through enhanced decision support tools is key to chronic management in cystic fibrosis at both the patient and care center level. Rapid decline in lung function relative to patient level and center norms is an important predictor of outcomes. Our objectives were to construct and utilize center-level classification of rapid decliners to develop an animated dashboard for comparisons within patients over time, multiple patients within centers, or between centers. A functional data analysis technique known as functional principal components analysis was applied to lung function trajectories from 18,387 patients across 247 accredited centers followed through the United States Cystic Fibrosis Foundation Patient Registry, in order to cluster patients into rapid decline phenotypes. Smaller centers (<30 patients) had older patients with lower baseline lung function and less severe rates of decline and had maximal decline later, compared to medium (30-150 patients) or large (>150 patients) centers. Small centers also had the lowest prevalence of early rapid decliners (17.7%, versus 24% and 25.7% for medium and large centers, resp.). The animated functional data analysis dashboard illustrated clustering and center-specific summaries of the rapid decline phenotypes. Clinical scenarios and utility of the center-level functional principal components analysis (FPCA) approach are considered and discussed.

Risk factor identification in cystic fibrosis by flexible hierarchical joint models.

Cystic fibrosis (CF) is a lethal autosomal disease hallmarked by respiratory failure. Maintaining lung function and minimizing frequency of acute respiratory events known as pulmonary exacerbations are essential to survival. Jointly modeling longitudinal lung function and exacerbation occurrences may provide better inference. We propose a shared-parameter joint hierarchical Gaussian process model with flexible link function to investigate the impacts of both demographic and time-varying clinical risk factors on lung function decline and to examine the associations between lung function and occurrence of pulmonary exacerbation. A two-level Gaussian process is used to capture the nonlinear longitudinal trajectory, and a flexible link function is introduced to the joint model in order to analyze binary process. Bayesian model assessment criteria are provided in examining the overall performance in joint models and marginal fitting in each submodel. We conduct simulation studies and apply the proposed model in a local CF center cohort. In the CF application, a nonlinear structure is supported in modeling both the longitudinal continuous and binary processes. A negative association is detected between lung function and pulmonary exacerbation by the joint model. The importance of risk factors, including gender, diagnostic status, insurance status, and BMI, is examined in joint models.

Flexible link functions in a joint hierarchical Gaussian process model.

Many longitudinal studies often require jointly modeling a biomarker and an event outcome, in order to provide more accurate inference and dynamic prediction of disease progression. Cystic fibrosis (CF) studies have illustrated the benefits of these models, primarily examining the joint evolution of lung-function decline and survival. We propose a novel joint model within the shared-parameter framework that accommodates nonlinear lung-function trajectories, in order to provide more accurate inference on lung-function decline over time and to examine the association between evolution of lung function and risk of a pulmonary exacerbation (PE) event recurrence. Specifically, a two-level Gaussian process (GP) is used to estimate the nonlinear longitudinal trajectories and a flexible link function is introduced for a more accurate depiction of the binary process on the event outcome. Bayesian model assessment is used to evaluate each component of the joint model in simulation studies and an application to longitudinal data on patients receiving care from a CF center. A nonlinear structure is suggested by both longitudinal continuous and binary evaluations. Including a flexible link function improves model fit to these data. The proposed hierarchical GP model with a flexible power link function where Laplace distribution is the baseline (spep) has the best fit of all joint models considered, characterizing how accelerated lung-function decline corresponds to increased odds of experiencing another PE.

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

BACKGROUND: Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support. OBJECTIVE: This study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians. METHODS: Longitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17). RESULTS: A clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application's potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions. CONCLUSIONS: Our framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool's effectiveness regarding increased communication, enhanced shared decision-making, and improved clinical outcomes for patients with CF.

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach.

Improving Detection of Rapid Cystic Fibrosis Disease Progression-Early Translation of a Predictive Algorithm Into a Point-of-Care Tool.

The clinical course of cystic fibrosis (CF) lung disease is marked by acute drops of lung function, defined clinically as rapid decline. As such, lung function is monitored routinely through pulmonary function testing, producing hundreds of measurements over the lifespan of an individual patient. Point-of-care technologies aimed at improving detection of rapid decline have been limited. Our aim in this early translational study is to develop and translate a predictive algorithm into a prototype prognostic tool for improved detection of rapid decline. The predictive algorithm was developed, validated and checked for 6-month, 1-year, and 2-year forecast accuracies using data on demographic and clinical characteristics from 30 879 patients aged 6 years and older who were followed in the U.S. Cystic Fibrosis Foundation Patient Registry from 2003 to 2015. Predictions of rapid decline based on the algorithm were compared to a detection algorithm currently being used at a CF center with 212 patients who received care between 2012-2017. The algorithm was translated into a prototype web application using RShiny, which resulted from an iterative development and refinement based on clinician feedback. The study showed that the algorithm had excellent predictive accuracy and earlier detection of rapid decline, compared to the current approach, and yielded a prototype platform with the potential to serve as a viable point-of-care tool. Future work includes implementation of this clinical prototype, which will be evaluated prospectively under real-world settings, with the aim of improving the pre-visit planning process for CF point of care. Likely extensions to other point-of-care settings are discussed.

Infant Weight and Length Growth Trajectories Modeled Using Superimposition by Translation and Rotation Are Differentially Associated with Body Composition Components at 3 and 7 Years of Age.

OBJECTIVE: To evaluate how infant weigh and length growth trajectories associate with body composition at 3 and 7 years because previous studies have noted that rapid infant weight gain increases risk for high body mass index (BMI) in children. STUDY DESIGN: There were 322 children enrolled at 3 years of age with dual x-ray absorptiometry body composition data and pediatrician growth data for 0-2 years of age who were included in analysis. Superimposition by translation and rotation modeling was used to characterize infant weight and length trajectories in terms of size, tempo and velocity measures. Associations of these measures with fat mass, lean mass, percent body fat, bone mineral content, BMI z-score, and overweight prevalence at 3 and 7 years of age were determined. RESULTS: Infant growth trajectories differed by sex, race, and breastfeeding status. Higher overall weight size and weight velocity from 0 to 2 years of age were associated positively with all age 3 body composition and anthropometry outcomes. However, longer length size from 0 to 2 years of age was associated independently with higher bone mineral content and lean mass, but lower percent body fat, BMI z-score, and a lower odds of overweight at 3 years of age. By 7 years of age, later than average infant weight tempo was also associated with lower fat mass, lean mass, and BMI z-score. CONCLUSIONS: Greater average weight size and greater weight velocity in infancy are markers for greater overall body size at 3 and 7 years of age. However, longer average lengths and later weight gain tempo between 0 and 2 years of age may help to establish a leaner body composition by 3 and 7 years of age.

Phenotypes of Rapid Cystic Fibrosis Lung Disease Progression during Adolescence and Young Adulthood.

RATIONALE: Individuals with cystic fibrosis are at risk for prolonged drops in lung function, clinically termed rapid decline, during discreet periods of the disease. OBJECTIVES: To identify phenotypes of rapid pulmonary decline and determine how these phenotypes are related to patient characteristics. METHODS: A longitudinal cohort study of patients with cystic fibrosis aged 6-21 years was conducted using the Cystic Fibrosis Foundation Patient Registry. A statistical approach for clustering longitudinal profiles, sparse functional principal components analysis, was used to classify patients into distinct phenotypes by evaluating trajectories of FEV1 decline. Phenotypes were compared with respect to baseline and mortality characteristics. MEASUREMENTS AND MAIN RESULTS: Three distinct phenotypes of rapid decline were identified, corresponding to early, middle, and late timing of maximal FEV1 loss, in the overall cohort (n = 18,387). The majority of variation (first functional principal component, 94%) among patient profiles was characterized by differences in mean longitudinal FEV1 trajectories. Average degree of rapid decline was similar among phenotypes (roughly -3% predicted/yr); however, average timing differed, with early, middle, and late phenotypes experiencing rapid decline at 12.9, 16.3, and 18.5 years of age, respectively. Individuals with the late phenotype had the highest initial FEV1 but experienced the greatest loss of lung function. The early phenotype was more likely to have respiratory infections and acute exacerbations at baseline or to develop them subsequently, compared with other phenotypes. CONCLUSIONS: By identifying phenotypes and associated risk factors, timing of interventions may be more precisely targeted for subgroups at highest risk of lung function loss.

Early Detection of Rapid Cystic Fibrosis Disease Progression Tailored to Point of Care: A Proof-of-Principle Study.

Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection-and possibly prevention-of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid lung function decline. The algorithm was developed using a novel longitudinal analysis of lung function (measured as forced expiratory volume in 1 s of % predicted, FEV1). Covariates included clinical and demographic characteristics selected from the registry based on information criterion. Preliminary assessment of algorithm performance suggested excellent predictive accuracy and earlier detection of rapid decline than standard of care being applied at a local center. Graphical displays were presented and evaluated for clinical utility. Predictions from the algorithms and chosen graphical displays were translated into a prototype web application using RShiny and underwent iterative development based on clinician feedback. This paper suggests that the algorithm and its translation could offer a means for earlier detection and treatment of rapid decline, providing clinicians with a viable point-of-care technology to intervene prior to irreversible lung damage.

The Chinese Mental Resilience Scale and its psychometric properties.

This study aimed to develop a Chinese Mental Resilience Scale. A total of 2500 healthy participants, in two representative samples of the Chinese population, were administered the scale. Exploratory factor analysis, confirmatory factor analysis, and correlation analysis were used to obtain the relevant coefficients and verify the reliability and validity of the scale. Five factors were extracted: willpower, family support, optimism and self-confidence, problem solving, and interpersonal interaction, plus a lying subscale, which together accounted for 54 percent of the total variance. The Chinese Mental Resilience Scale demonstrated good psychometric properties. It can be used to evaluate the mental resilience level of general Chinese population.

Dynamics of Disease Progression and Gastrostomy Tube Placement in Children and Adolescents with Cystic Fibrosis: Application of Joint Models for Longitudinal and Time-to-Event Data.

BACKGROUND: Previous small-sample studies have examined the effect of gastrostomy (g-) tube placement on weight, height, and lung function in adolescent patients with cystic fibrosis (CF), but there are no RCTs to date reporting efficacy. The goal of this study was to implement a dynamic prediction model to 1) understand the role of rapid lung function decline in g-tube placement in real-world clinical settings; 2) provide a prognostic tool with the potential to aid clinicians in optimizing the timing of g-tube placement, in relation to rate of lung function decline and current nutrition status. METHODS: A dynamic prediction model was developed, utilizing data on patients 6-21 years of age from the Cystic Fibrosis Foundation Patient Registry (1997-2013). A joint model was implemented, which coupled a semiparametric mixed model to characterize rapid lung function decline with a time-to-event model to identify risk factors for g-tube initiation. RESULTS: The 4,034 individuals (21.3%) who underwent g-tube placement during adolescence or young adulthood had poorer nutrition and lung function at baseline and initially had increased rates of pancreatic enzyme use, infection and gastroesophageal reflux disease, compared to those who did not receive g-tubes; these associations changed over follow up. Rapid lung function decline was associated with increased risk of g-tube supplementation. CONCLUSIONS: By jointly modeling longitudinal patterns of lung function decline with g-tube delivery, it is possible to construct prognostic aids to evaluate treatment delivery in relation to the onset of rapid lung function decline and other important clinical markers. These algorithms have the potential to enable more effective monitoring of disease progression and promote more timely treatment delivery.