Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy.

BACKGROUND: Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake. METHODS: We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level. RESULTS: Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with standard deviations of 0.29 and 0.20. The predictive accuracy was robust across different covariate specifications. CONCLUSIONS: The methods and applications of dynamic prediction models developed using data prior to modulator uptake have the potential to inform post-modulator projections of lung function and enhance clinical surveillance in the new era of CF care.

Predicting weight gain in patients with cystic fibrosis on triple combination modulator.

BACKGROUND: Cystic fibrosis (CF) is caused by CF transmembrane conductance regulator (CFTR) gene mutations producing dysfunctional CFTR proteins leading to progressive clinical disease. Elexacaftor-tezacaftor-ivacaftor (ETI) remarkably improves lung disease but is associated with substantial weight gain. STUDY DESIGN AND METHODS: We performed a single-center longitudinal study predicting 6-month weight gain after ETI initiation. We used linear mixed effects modeling (LME) to determine association of ETI treatment with changing body mass index (BMI). Using linear regression, we examined BMI prediction models with distinct combinations of main effects to identify a model useful for patient counseling. We used up to eight commonly observed clinical characteristics as input variables (age, sex, percent predicted FEV1 [FEV1%], F508del homozygous state, pancreatic sufficiency, HgbA1c, prior modulator use and prior year number of pulmonary exacerbations). RESULTS: We evaluated 154 patients (19-73 years old, 54% female, FEV1% = 19-121, 0-6 prior year pulmonary exacerbations). LME demonstrated an association between ETI use and weight increases. Exhaustive testing suggested a parsimonious linear regression model well-fitted to data that is potentially useful for counseling. The two variable model shows that on average, BMI decreases by 0.045 (95% Confidence Interval [CI] = -0.069 to -0.021, p < 0.001) for every year of age and increases by 0.322 (CI = 0.142 to 0.502, p = 0.001) for each additional prior year exacerbation at the time of ETI initiation. INTERPRETATION: Young patients with many prior year pulmonary exacerbations likely have the largest 6 month weight gain after starting ETI.

The impact of switching to race-neutral reference equations on FEV1 percent predicted among people with cystic fibrosis.

RATIONALE: The American Thoracic Society recommended switching to race-neutral spirometry reference equations, as race is a social construct and to avoid normalizing disparities in lung function due to structural racism. Understanding the impact of the race-neutral equations on percent predicted forced expiratory volume in one second (ppFEV1) in people with cystic fibrosis (PwCF) will help prepare patients and providers to interpret pulmonary function test results. OBJECTIVE(S): To quantify the impact of switching from Global Lung Initiative (GLI) 2012 race-specific to GLI 2022 Global race-neutral reference equations on the distribution of ppFEV1 among PwCF of different races. METHODS: Cross-sectional analysis of FEV1 among PwCF ages ≥6 years in the 2021 U.S. Cystic Fibrosis Foundation Patient Registry. We describe the absolute difference in ppFEV1 between the two reference equations by reported race and the effect of age and height on this difference. RESULTS: With the switch to GLI Global, ppFEV1 will increase for White (median increase 4.7, (IQR: 3.1; 6.4)) and Asian (2.6 (IQR: 1.6; 3.7)) individuals and decrease for Black individuals (-7.7, (IQR: -10.9; -5.2)). Other race categories will see minimal changes in median ppFEV1. Individuals with higher baseline ppFEV1 and younger age will see a greater change in ppFEV1 (i.e., a greater improvement among White and Asian individuals and a greater decline among Black individuals). CONCLUSIONS: Switching from GLI 2012 race-specific reference equations to GLI 2022 Global race-neutral equations will result in larger reductions in ppFEV1 among Black individuals with CF than increases among White and Asian people with CF.

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

Airway inflammation accelerates pulmonary exacerbations in cystic fibrosis.

Airway inflammation underlies cystic fibrosis (CF) pulmonary exacerbations. In a prospective multicenter study of randomly selected, clinically stable adolescents and adults, we assessed relationships between 24 inflammation-associated molecules and the future occurrence of CF pulmonary exacerbation using proportional hazards models. We explored relationships for potential confounding or mediation by clinical factors and assessed sensitivities to treatments including CF transmembrane regulator (CFTR) protein synthesis modulators. Results from 114 participants, including seven on ivacaftor or lumacaftor-ivacaftor, representative of the US CF population during the study period, identified 10 biomarkers associated with future exacerbations mediated by percent predicted forced expiratory volume in 1 s. The findings were not sensitive to anti-inflammatory, antibiotic, and CFTR modulator treatments. The analyses suggest that combination treatments addressing RAGE-axis inflammation, protease-mediated injury, and oxidative stress might prevent pulmonary exacerbations. Our work may apply to other airway inflammatory diseases such as bronchiectasis and the acute respiratory distress syndrome.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

BACKGROUND AND OBJECTIVES: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation. METHODS: In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery. RESULTS: A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations. DISCUSSION: ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.

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

Background: Cystic fibrosis (CF) is a genetic disease but is greatly impacted by non-genetic (social/environmental and stochastic) influences. Some people with CF experience rapid decline, a precipitous drop in lung function relative to patient- and/or center-level norms. Those who experience rapid decline in early adulthood, compared to adolescence, typically exhibit less severe clinical disease but greater loss of lung function. The extent to which timing and degree of rapid decline are informed by social and environmental determinants of health (geomarkers) is unknown. Methods: A longitudinal cohort study was performed (24,228 patients, aged 6-21 years) using the U.S. CF Foundation Patient Registry. Geomarkers at the ZIP Code Tabulation Area level measured air pollution/respiratory hazards, greenspace, crime, and socioeconomic deprivation. A composite score quantifying social-environmental adversity was created and used in covariate-adjusted functional principal component analysis, which was applied to cluster longitudinal lung function trajectories. Results: Social-environmental phenotyping yielded three primary phenotypes that corresponded to early, middle, and late timing of peak decline in lung function over age. Geographic differences were related to distinct cultural and socioeconomic regions. Extent of peak decline, estimated as forced expiratory volume in 1 s of % predicted/year, ranged from 2.8 to 4.1 % predicted/year depending on social-environmental adversity. Middle decliners with increased social-environmental adversity experienced rapid decline 14.2 months earlier than their counterparts with lower social-environmental adversity, while timing was similar within other phenotypes. Early and middle decliners experienced mortality peaks during early adolescence and adulthood, respectively. Conclusion: While early decliners had the most severe CF lung disease, middle and late decliners lost more lung function. Higher social-environmental adversity associated with increased risk of rapid decline and mortality during young adulthood among middle decliners. This sub-phenotype may benefit from enhanced lung-function monitoring and personalized secondary environmental health interventions to mitigate chemical and non-chemical stressors.

Bayesian causal inference for observational studies with missingness in covariates and outcomes.

Missing data are a pervasive issue in observational studies using electronic health records or patient registries. It presents unique challenges for statistical inference, especially causal inference. Inappropriately handling missing data in causal inference could potentially bias causal estimation. Besides missing data problems, observational health data structures typically have mixed-type variables - continuous and categorical covariates - whose joint distribution is often too complex to be modeled by simple parametric models. The existence of missing values in covariates and outcomes makes the causal inference even more challenging, while most standard causal inference approaches assume fully observed data or start their works after imputing missing values in a separate preprocessing stage. To address these problems, we introduce a Bayesian nonparametric causal model to estimate causal effects with missing data. The proposed approach can simultaneously impute missing values, account for multiple outcomes, and estimate causal effects under the potential outcomes framework. We provide three simulation studies to show the performance of our proposed method under complicated data settings whose features are similar to our case studies. For example, Simulation Study 3 assumes the case where missing values exist in both outcomes and covariates. Two case studies were conducted applying our method to evaluate the comparative effectiveness of treatments for chronic disease management in juvenile idiopathic arthritis and cystic fibrosis.

Association of Pseudomonas aeruginosa infection stage with lung function trajectory in children with cystic fibrosis.

BACKGROUND: Pseudomonas aeruginosa (Pa) infection in cystic fibrosis (CF) is characterized in stages: never (prior to first positive culture) to incident (first positive culture) to chronic. The association of Pa infection stage with lung function trajectory is poorly understood and the impact of age on this association has not been examined. We hypothesized that FEV1 decline would be slowest prior to Pa infection, intermediate after incident infection and greatest after chronic Pa infection. METHODS: Participants in a large US prospective cohort study diagnosed with CF prior to age 3 contributed data through the U.S. CF Patient Registry. Cubic spline linear mixed effects models were used to evaluate the longitudinal association of Pa stage (never, incident, chronic using 4 different definitions) with FEV1 adjusted for relevant covariates. Models contained interaction terms between age and Pa stage. RESULTS: 1,264 subjects born 1992-2006 provided a median 9.5 (IQR 0.25 to 15.75) years of follow up through 2017. 89% developed incident Pa; 39-58% developed chronic Pa depending on the definition. Compared to never Pa, incident Pa infection was associated with greater annual FEV1 decline and chronic Pa infection with the greatest FEV1 decline. The most rapid FEV1 decline and strongest association with Pa infection stage was seen in early adolescence (ages 12-15). CONCLUSIONS: Annual FEV1 decline worsens significantly with each Pa infection stage in children with CF. Our findings suggest that measures to prevent chronic infection, particularly during the high-risk period of early adolescence, could mitigate FEV1 decline and improve survival.

Multilevel joint model of longitudinal continuous and binary outcomes for hierarchically structured data.

Joint modeling has been a useful strategy for incorporating latent associations between different types of outcomes simultaneously, often focusing on a longitudinal continuous outcome characterized by an LME submodel and a terminal event subject to a Cox proportional hazard or parametric survival submodel. Applications to hierarchical longitudinal studies have been less frequent, particularly with respect to a binary process, which is commonly specified by a GLMM. Furthermore, many of the joint model developments have not allowed for investigations of nested effects, such as those arising from multicenter studies. To fill this gap, we propose a multilevel joint model that encompasses the LME submodel and GLMM through a Bayesian approach. Motivated by the need for timely detection of pulmonary exacerbation and characterization of irregularly observed lung function measurements in people living with cystic fibrosis (CF) receiving care across multiple centers, we apply the model to the data arising from US CF Foundation Patient Registry. In parallel, we examine the extent of bias induced by a non-hierarchical model. Our simulation study and application results show that incorporating the center effect along with individual stochastic variation over time within the LME submodel improves model estimation and prediction. Given that the center effect is evident in lung function observed in the CF population, accounting for center-specific power parameters by incorporating the symmetric power exponential power (spep) link function in the GLMM can facilitate more accurate conclusions in clinical studies.

Lung function and secondhand smoke exposure among children with cystic fibrosis: A Bayesian meta-analysis.

BACKGROUND: Secondhand smoke exposure, an important environmental health factor in cystic fibrosis (CF), remains uniquely challenging to children with CF as they strive to maintain pulmonary function during early stages of growth and throughout adolescence. Despite various epidemiologic studies among CF populations, little has been done to coalesce estimates of the association between secondhand smoke exposure and lung function decline. METHODS: A systematic review was performed using PRISMA guidelines. A Bayesian random-effects model was employed to estimate the association between secondhand smoke exposure and change in lung function (measured as FEV1% predicted). RESULTS: Quantitative synthesis of study estimates indicated that second-hand smoke exposure corresponded to a significant drop in FEV1 (estimated decrease: -5.11% predicted; 95% CI: -7.20, -3.47). The estimate of between-study heterogeneity was 1.32% predicted (95% CI: 0.05, 4.26). There was moderate heterogeneity between the 6 analyzed studies that met review criteria (degree of heterogeneity: I2=61.9% [95% CI: 7.3-84.4%] and p = 0.022 from the frequentist method.) CONCLUSIONS: Our results quantify the impact at the pediatric population level and corroborate the assertion that secondhand smoke exposure negatively affects pulmonary function in children with CF. Findings highlight challenges and opportunities for future environmental health interventions in pediatric CF care.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

OBJECTIVE: To determine whether automated, electronic alerts increased referrals for epilepsy surgery. METHODS: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit. Patients classified as a potential surgical candidate were randomized 2:1 for their provider to receive an alert or standard of care (no alert). The primary outcome was referral for a neurosurgical evaluation. The likelihood of referral was estimated using a Cox proportional hazards regression model. RESULTS: Between April 2017 and April 2019, at total of 4858 children were screened by the system, and 284 (5.8%) were identified as potential surgical candidates. Two hundred four patients received an alert, and 96 patients received standard care. Median follow-up time was 24 months (range: 12-36 months). Compared to the control group, patients whose provider received an alert were more likely to be referred for a presurgical evaluation (3.1% vs 9.8%; adjusted hazard ratio [HR] = 3.21, 95% confidence interval [CI]: 0.95-10.8; one-sided p = .03). Nine patients (4.4%) in the alert group underwent epilepsy surgery, compared to none (0%) in the control group (one-sided p = .03). SIGNIFICANCE: Machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.

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.

Predicting Individualized Lung Disease Progression in Treatment-Naive Patients With Lymphangioleiomyomatosis.

BACKGROUND: Lung function decline varies significantly in patients with lymphangioleiomyomatosis (LAM), impeding individualized clinical decision-making. RESEARCH QUESTION: Can we aid individualized decision-making in LAM by developing a dynamic prediction model that can estimate the probability of clinically relevant FEV1 decline in patients with LAM before treatment initiation? STUDY DESIGN AND METHODS: Patients observed in the US National Heart, Lung, and Blood Institute (NHLBI) Lymphangioleiomyomatosis Registry were included. Using routinely available variables such as age at diagnosis, menopausal status, and baseline lung function (FEV1 and diffusing capacity of the lungs for carbon monoxide [Dlco]), we used novel stochastic modeling and evaluated predictive probabilities for clinically relevant drops in FEV1. We formed predictive probabilities of transplant-free survival by jointly modeling longitudinal FEV1 and lung transplantation or death events. External validation used the UK Lymphangioleiomyomatosis Natural History cohort. RESULTS: Analysis of the NHLBI Lymphangioleiomyomatosis Registry and UK Lymphangioleiomyomatosis Natural History cohorts consisted of 216 and 185 individuals, respectively. We derived a joint model that accurately estimated the risk of future lung function decline and 5-year probabilities of transplant-free survival in patients with LAM not taking sirolimus (area under the receiver operating characteristic curve [AUC], approximately 0.80). The prediction model provided estimates of forecasted FEV1, rate of FEV1 decline, and probabilities for risk of prolonged drops in FEV1 for untreated patients with LAM with a high degree of accuracy (AUC > 0.80) for the derivation cohort as well as the validation cohort. Our tool is freely accessible at: https://anushkapalipana.shinyapps.io/testapp_v2/. INTERPRETATION: Longitudinal modeling of routine clinical data can allow individualized LAM prognostication and assist in decision-making regarding the timing of treatment initiation.

Body composition and functional correlates of CF youth experiencing pulmonary exacerbation and recovery.

BACKGROUND: Youth with cystic fibrosis (CF) and pulmonary exacerbation (PEx) often experience weight loss, then rapid weight gain. Little is known about body composition and its relationship to functional outcomes during this critical period. METHODS: Twenty CF youth experiencing PEx were assessed on the day following admission and 7-17 days later at discharge for body mass index (BMI), fat mass index (FMI), lean mass index (LMI), skeletal muscle mass index (SMMI), and functional measures: percent predicted forced expiratory volume in 1 second (FEV1) (ppFEV1), maximal inspiratory and expiratory pressures (MIPs and MEPs), and handgrip strength (HGS). Changes from admission to discharge and correlations among body composition indices and functional measures at both times are reported. RESULTS: Upon admission, participant BMI percentile and ppFEV1 varied from 2 to 97 and 29 to 113, respectively. Thirteen had an LMI below the 25th percentile and nine had a percent body fat above the 75th percentile. BMI and FMI increased significantly (p = 0.03, 0.003) during hospitalization. LMI and SMMI did not change. FEV1 and MIPS increased (p = 0.0003, 0.007), independent of weight gain, during treatment. HGS did not improve. CONCLUSIONS: Many youth with CF, independent of BMI, frequently carried a small muscle mass and disproportionate fat at the time of PEx. During hospital treatment, weight gain largely represented fat deposition; muscle mass and strength did not improve. A need for trials of interventions designed to augment muscle mass and function, and limit fat mass accretion, at the time of PEx is suggested by these observations.

CellDrift: inferring perturbation responses in temporally sampled single-cell data.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

Real-world Associations of US Cystic Fibrosis Newborn Screening Programs With Nutritional and Pulmonary Outcomes.

Importance: Newborn screening (NBS) for cystic fibrosis (CF) has been universal in the US since 2010, but its association with clinical outcomes is unclear. Objective: To describe the real-world effectiveness of NBS programs for CF in the US on outcomes up to age 10 years. Design, Setting, and Participants: This was a retrospective cohort study using CF Foundation Patient Registry data from January 1, 2000, to December 31, 2018. The staggered implementation of NBS programs by state was used to compare longitudinal outcomes among children in the same birth cohort born before vs after the implementation of NBS for CF in their state of birth. Participants included children with an established diagnosis of CF born between January 1, 2000, to December 31, 2018, in any of the 44 states that implemented NBS for CF between 2003 and 2010. Data were analyzed from October 5, 2020, to April 22, 2022. Exposures: Birth before vs after the implementation of NBS for CF in the state of birth. Main Outcomes and Measures: Longitudinal trajectory of height and weight percentiles from diagnosis, lung function (forced expiratory volume in 1 second, [FEV1] percent predicted) from age 6 years, and age at initial and chronic infection with Pseudomonas aeruginosa using linear mixed-effects and time-to-event models adjusting for birth cohort and potential confounders. Results: A total of 9571 participants (4713 female participants [49.2%]) were eligible for inclusion, with 4510 (47.1%) in the pre-NBS cohort. NBS was associated with higher weight and height percentiles in the first year of life (weight, 6.0; 95% CI, 3.1-8.4; height, 6.6; 95% CI, 3.8-9.3), but these differences decreased with age. There was no association between NBS and FEV1 at age 6 years, but the percent-predicted FEV1 did increase more rapidly with age in the post-NBS cohort. NBS was associated with older age at chronic P aeruginosa infection (hazard ratio, 0.69; 95% CI, 0.54-0.89) but not initial P aeruginosa infection (hazard ratio, 0.88; 95% CI, 0.77-1.01). Conclusions and Relevance: NBS for CF in the US was associated with improved nutritional status up to age 10 years, a more rapid increase in lung function, and delayed chronic P aeruginosa infection. In the future, as highly effective modulator therapies become available for infants with CF, NBS will allow for presymptomatic initiation of these disease-modifying therapies before irreversible organ damage.

Tissue-localized immune responses in people with cystic fibrosis and respiratory nontuberculous mycobacteria infection.

Nontuberculous mycobacteria (NTM) are an increasingly common cause of respiratory infection in people with cystic fibrosis (PwCF). Relative to those with no history of NTM infection (CF-NTMNEG), PwCF and a history of NTM infection (CF-NTMPOS) are more likely to develop severe lung disease and experience complications over the course of treatment. In other mycobacterial infections (e.g., tuberculosis), an overexuberant immune response causes pathology and compromises organ function; however, since the immune profiles of CF-NTMPOS and CF-NTMNEG airways are largely unexplored, it is unknown which, if any, immune responses distinguish these cohorts or concentrate in damaged tissues. Here, we evaluated lung lobe-specific immune profiles of 3 cohorts (CF-NTMPOS, CF-NTMNEG, and non-CF adults) and found that CF-NTMPOS airways are distinguished by a hyperinflammatory cytokine profile. Importantly, the CF-NTMPOS airway immune profile was dominated by B cells, classical macrophages, and the cytokines that support their accumulation. These and other immunological differences between cohorts, including the near absence of NK cells and complement pathway members, were enriched in the most damaged lung lobes. The implications of these findings for our understanding of lung disease in PwCF are discussed, as are how they may inform the development of host-directed therapies to improve NTM disease treatment.