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Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rationale: Declines in percent predicted forced expiratory volume in 1 second (ppFEV 1 ) are an important marker of clinical progression of cystic fibrosis (CF). Objectives: We examined ppFEV 1 variability in relation to a combined outcome of lung transplant or death. Methods: We estimated the association between ppFEV 1 variability and the combined outcome of lung transplant or death. We included children aged 8 years and older with CF and two prior years of ppFEV 1 data before baseline between 2005 and 2021. We defined ppFEV 1 increased variability as any relative increase or decrease of at least 10% in ppFEV 1 from a 2-year averaged baseline. A marginal structural Cox proportional hazards model was used. We examined a cumulative measure of ppFEV 1 variability, defined as the cumulative proportion of visits with ppFEV 1 variability at each visit. Kaplan-Meier survival curves were generated on the basis of quartiles of the cumulative distribution of ppFEV 1 variability. Results: We included 9,706 patients with CF in our cohort. The median age at cohort entry was 8.3 (interquartile range, 8.2-8.4) years; 50% of patients were female; 94% were White; and the median baseline ppFEV 1 was 94.4 (interquartile range, 81.6-106.1). The unadjusted hazard ratio for increased ppFEV 1 variability on lung transplant/mortality was 4.13 (95% confidence interval, 3.48-4.90), and the weighted hazard ratio was 1.49 (95% confidence interval, 1.19-1.86). Survival curves stratified by quartile of cumulative variability demonstrated an increased hazard of lung transplant/mortality as the proportion of cumulative ppFEV 1 variability increased. Conclusions: We found a strong association between ppFEV 1 variability and lung transplant or mortality in a cohort of people with CF in the United States.

Introduction: Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities., Methods: We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance., Results: The precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race., Conclusions: Racial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it., Competing Interests: None., (© The Author(s) 2024.)

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 (ppFEV 1 ) 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 ppFEV 1 among PwCF of different races., Methods: Cross-sectional analysis of FEV 1 among PwCF ages ≥6 years in the 2021 U.S. Cystic Fibrosis Foundation Patient Registry. We describe the absolute difference in ppFEV 1 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, ppFEV 1 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 ppFEV 1 . Individuals with higher baseline ppFEV 1 and younger age will see a greater change in ppFEV 1 (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 ppFEV 1 among Black individuals with CF than increases among White and Asian people with CF., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Elizabeth Cromwell reports financial support was provided by Cystic Fibrosis Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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., (© 2024. The Author(s).)

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.

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., Competing Interests: T.G.L., J.A.F., J.L.J., Y.L., K.A.P., and J.B.V. received other support from the CFF (CC132-16AD, LIOU14Y0, LIOU14P0) and the National Heart Lung and Blood Institute (NHLBI) of the National Institutes of Health (NIH) (R01 HL125520) and received support during the current study for performing clinical trials from Abbvie, Calithera Biosciences, Corbus Pharmaceuticals, Gilead Sciences, Laurent Pharmaceuticals, Nivalis Therapeutics, Novartis, Proteostasis, Savara Pharmaceuticals, Translate Bio, and Vertex Pharmaceuticals. F.R.A. received additional other support from the NHLBI/NIH (R01 HL125520), the National Science Foundation (EMSW21-RTG), and the Margolis Family Foundation of Utah. P.S.B. received other support from the CFF (Center and TDC grants) and the NHLBI/NIH (U01 HL114623) and received support for a clinical trial from Alcresta Therapeutics. B.A.C. received other support from the CFF (C112–12, C112-TDC09Y, 10063SUB, 41339154.s132P010379SUB) and received support for clinical trials from Genentech, Novartis, and Vertex Pharmaceuticals. C.L.D. received other support from the CFF (C004–11, C004-TDC09Y, DAINES11Y3) and from the Health Resources and Services Administration (T72MC00012). J.A.F. transitioned to become an employee of ICON plc, a clinical research organization involved in various trials pertinent to CF during the study and is now an employee of Vertex Pharmaceuticals; JAF, ICON, and Vertex had no direct involvement in performance of the study following the initial change in affiliation. T.H. received other support from the CFF (PACE, Center Grant) and received support for clinical trials from Celtaxsys and Vertex Pharmaceuticals. J.R.H. received other support from the NHLBI/NIH (HHSN268200900018C) and the Veterans Administration Healthcare System (I01 BX001533). J.L. received other support from the CFF (C017-11AF). C.N. received other support from the CFF (C138-12). P.R. received other support from the CFF (C003–12, C003-TDC09Y). S.D.S. received other support from the CFF (AQUADEK12K1, SAGEL11CS0, GOAL13K2, NICK13A0, SAGEL14K1, NICK15R0) and the NHLBI/NIH (U54 HL096458) and the NCATS/NIH (Colorado CTSA Grant Number UL1 TR002535). J.L.T.-C. received other support from the CFF (TDC) and the NHLBI/NIH (HL103801) and received support for clinical trials from Vertex Pharmaceuticals. K.N.O. was funded by the intramural research program of the NHLBI, NIH, during the study. Neither the project sponsors nor any sources of other support had direct roles in development and conduct of the study. None of the sponsors of clinical trials mentioned earlier participated in any way with this trial., (© 2024 The Author(s).)

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., Competing Interests: Declaration of Competing Interest Author RDS serves on the Cystic Fibrosis Foundation Patient Registry Committee. The remaining authors have no conflicts of interest to report.

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., (© 2023 John Wiley & Sons Ltd.)

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 FEV 1 % predicted)., Results: Quantitative synthesis of study estimates indicated that second-hand smoke exposure corresponded to a significant drop in FEV 1 (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: I 2 =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., Competing Interests: Declaration of Competing Interest Author RS serves on the editorial board of the Journal of Cystic Fibrosis. All other authors disclose no conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

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., (© 2023 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

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 FEV 1 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 (FEV 1 and diffusing capacity of the lungs for carbon monoxide [Dlco]), we used novel stochastic modeling and evaluated predictive probabilities for clinically relevant drops in FEV 1 . We formed predictive probabilities of transplant-free survival by jointly modeling longitudinal FEV 1 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 FEV 1 , rate of FEV 1 decline, and probabilities for risk of prolonged drops in FEV 1 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., (Published by Elsevier Inc.)

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 (FEV 1 ) 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/m 3 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., (© 2023 The Authors. Pediatric Pulmonology published by Wiley Periodicals LLC.)

Background: Cystic fibrosis (CF) is a life-threatening genetic disease, affecting around 10 500 people in the UK. Precision medicines have been developed to treat specific CF-gene mutations. The newest, elexacaftor/tezacaftor/ivacaftor (ELEX/TEZ/IVA), has been found to be highly effective in randomised controlled trials (RCTs) and became available to a large proportion of UK CF patients in 2020. Understanding the potential health economic impacts of ELEX/TEZ/IVA is vital to planning service provision., Methods: We combined observational UK CF Registry data with RCT results to project the impact of ELEX/TEZ/IVA on total days of intravenous (IV) antibiotic treatment at a population level. Registry data from 2015 to 2017 were used to develop prediction models for IV days over a 1-year period using several predictors, and to estimate 1-year population total IV days based on standards of care pre-ELEX/TEZ/IVA. We considered two approaches to imposing the impact of ELEX/TEZ/IVA on projected outcomes using effect estimates from RCTs: approach 1 based on effect estimates on FEV 1 % and approach 2 based on effect estimates on exacerbation rate., Results: ELEX/TEZ/IVA is expected to result in significant reductions in population-level requirements for IV antibiotics of 16.1% (~17 800 days) using approach 1 and 43.6% (~39 500 days) using approach 2. The two approaches require different assumptions. Increased understanding of the mechanisms through which ELEX/TEZ/IVA acts on these outcomes would enable further refinements to our projections., Conclusions: This work contributes to increased understanding of the changing healthcare needs of people with CF and illustrates how Registry data can be used in combination with RCT evidence to estimate population-level treatment impacts., Competing Interests: Competing interests: SBC reports personal fees and other from Chiesi Pharmaceuticals, non-financial support and other from Vertex, other from Zambon, other from Insmed, outside the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

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.

This study proposes a Bayesian joint model with extended random effects structure that incorporates nested repeated measures and provides simultaneous inference on treatment effects over time and drop-out patterns. The proposed model includes flexible splines to characterize the circadian variation inherent in blood pressure sequences, and we assess the effectiveness of an intervention to resolve pediatric obstructive sleep apnea. We demonstrate that the proposed model and its conventional two-stage counterpart provide similar estimates of nighttime blood pressure but estimates on the mean evolution of daytime blood pressure are discrepant. Our simulation studies tailored to the motivating data suggest reasonable estimation and coverage probabilities for both fixed and random effects. Computational challenges of model implementation are discussed., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2022 Enas M. Ghulam et al.)

In omics experiments, estimation and variable selection can involve thousands of proteins/genes observed from a relatively small number of subjects. Many regression regularization procedures have been developed for estimation and variable selection in such high-dimensional problems. However, approaches have predominantly focused on linear regression models that ignore correlation arising from long sequences of repeated measurements on the outcome. Our work is motivated by the need to identify proteomic biomarkers that improve the prediction of rapid lung-function decline for individuals with cystic fibrosis (CF) lung disease. We extend four Bayesian penalized regression approaches for a Gaussian linear mixed effects model with nonstationary covariance structure to account for the complicated structure of longitudinal lung function data while simultaneously estimating unknown parameters and selecting important protein isoforms to improve predictive performance. Different types of shrinkage priors are evaluated to induce variable selection in a fully Bayesian framework. The approaches are studied with simulations. We apply the proposed method to real proteomics and lung-function outcome data from our motivating CF study, identifying a set of relevant clinical/demographic predictors and a proteomic biomarker for rapid decline of lung function. We also illustrate the methods on CD4 yeast cell-cycle genomic data, confirming that the proposed method identifies transcription factors that have been highlighted in the literature for their importance as cell cycle transcription factors., (© 2021 John Wiley & Sons Ltd.)

Rationale: A previous analysis found significantly higher lung function in the US paediatric cystic fibrosis (CF) population compared with the UK with this difference apparently decreasing in adolescence and adulthood. However, the cross-sectional nature of the study makes it hard to interpret these results., Objectives: To compare longitudinal trajectories of lung function in children with CF between the USA and UK and to explore reasons for any differences., Methods: We used mixed effects regression analysis to model lung function trajectories in the study populations. Using descriptive statistics, we compared early growth and nutrition (height, weight, body mass index), infections ( Pseudomonas aeruginosa , Staphylococcus aureus ) and treatments (rhDnase, hypertonic saline, inhaled antibiotics)., Results: We included 9463 children from the USA and 3055 children from the UK with homozygous F508del genotype. Lung function was higher in the USA than in the UK when first measured at age six and remained higher throughout childhood. We did not find important differences in early growth and nutrition, or P.aeruginosa infection. Prescription of rhDNase and hypertonic saline was more common in the USA. Inhaled antibiotics were prescribed at similar levels in both countries, but Tobramycin was prescribed more in the USA and colistin in the UK. S. aureus infection was more common in the USA than the UK., Conclusions: Children with CF and homozygous F508del genotype in the USA had better lung function than UK children. These differences do not appear to be explained by early growth or nutrition, but differences in the use of early treatments need further investigation., Competing Interests: Competing interests: DKS, SC and DT-R were supported by the Strategic Research Centre 'CF-EpiNet: Harnessing data to improve lives' funded by the Cystic Fibrosis Trust. DT-R is funded by the MRC on a Clinician Scientist Fellowship (MR/P008577/1). RS was supported by grants from the Cystic Fibrosis Foundation (SZCZES18AB0) and NIH/NHLBI (R01 HL141286). CHG was supported by grants from the Cystic Fibrosis Foundation, the NIH (UM1 HL119073, P30 DK089507, U01 HL114589, UL1 TR000423) and the FDA (R01 FD003704). RHK is supported by a UKRI Future Leaders Fellowship (MR/S017968/1)., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: People with cystic fibrosis (PWCF) suffer from acute unpredictable reductions in pulmonary function associated with a pulmonary exacerbation (PEx) that may require hospitalization. PEx symptoms vary between PWCF without universal diagnostic criteria for diagnosis and response to treatment., Research Question: We characterized sweat metabolomes before and after intravenous (IV) antibiotics in PWCF hospitalized for PEx to determine feasibility and define biological alterations by IV antibiotics for PEx., Study Design and Methods: PWCF with PEx requiring hospitalization for IV antibiotics were recruited from clinic. Sweat samples were collected using the Macroduct® Sweat Collection System at admission prior to initiation of IV antibiotics and after completion prior to discharge. Samples were analyzed for metabolite changes using ultra-high-performance liquid chromatography/tandem accurate mass spectrometry., Results: Twenty-six of 29 hospitalized PWCF completed the entire study. A total of 326 compounds of known identity were detected in sweat samples. Of detected metabolites, 147 were significantly different between pre-initiation and post-completion of IV antibiotics for PEx (average treatment 14 days). Global sweat metabolomes changed from before and after IV antibiotic treatment. We discovered specific metabolite profiles predictive of PEx status as well as enriched biologic pathways associated with PEx. However, metabolomic changes were similar in PWCF who failed to return to baseline pulmonary function and those who did not., Interpretation: Our findings demonstrate the feasibility of non-invasive sweat metabolomic profiling in PWCF and the potential for sweat metabolomics as a prospective diagnostic and research tool to further advance our understanding of PEx in PWCF., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Characterizing seasonal trend in lung function in individuals with chronic lung disease may lead to timelier treatment of acute respiratory symptoms and more precise distinction between seasonal exposures and variability. Limited research has been conducted to assess localized seasonal fluctuation in lung function decline in individuals with cystic fibrosis (CF) in context with routinely collected demographic and clinical data. We conducted a longitudinal cohort study of 253 individuals aged 6-22 years with CF receiving care at a pediatric Midwestern US CF center with median (range) of follow-up time of 4.7 (0-9.95) years, implementing two distinct models to estimate seasonality effects. The outcome, lung function, was measured as percent-predicted of forced expiratory volume in 1 second (FEV 1 ). Both models showed that older age, being male, using Medicaid insurance and having Pseudomonas aeruginosa infection corresponded to accelerated FEV 1 decline. A sine wave model for seasonality had better fit to the data, compared to a linear model with categories for seasonality. Compared to international cohorts, seasonal fluctuations occurred earlier and with greater volatility, even after adjustment for ambient temperature. Average lung function peaked in February and dipped in August, and FEV1 fluctuation was 0.81 % predicted (95% CI: 0.52 to 1.1). Adjusting for temperature shifted the peak and dip to March and September, respectively, and decreased FEV 1 fluctuation to 0.45 % predicted (95% CI: 0.08 to 0.82). Understanding localized seasonal variation and its impact on lung function may allow researchers to perform precision public health for lung diseases and disorders at the point-of-care level.

Objectives: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery., Materials & Methods: In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, procedures, laboratories, and demographic information. Site-specific algorithms were developed at two epilepsy centers: one pediatric and one adult. Cases were defined as patients who underwent resective epilepsy surgery, and controls were patients with epilepsy with no history of surgery. The output of the ML algorithms was the estimated likelihood of candidacy for resective epilepsy surgery. Model performance was assessed using 10-fold cross-validation., Results: There were 5880 children (n = 137 had surgery [2.3%]) and 7604 adults with epilepsy (n = 56 had surgery [0.7%]) included in the study. Pediatric surgical patients could be identified 2.0 years (range: 0-8.6 years) before beginning their presurgical evaluation with AUC =0.76 (95% CI: 0.70-0.82) and PR-AUC =0.13 (95% CI: 0.07-0.18). Adult surgical patients could be identified 1.0 year (range: 0-5.4 years) before beginning their presurgical evaluation with AUC =0.85 (95% CI: 0.78-0.93) and PR-AUC =0.31 (95% CI: 0.14-0.48). By the time patients began their presurgical evaluation, the ML algorithms identified pediatric and adult surgical patients with AUC =0.93 and 0.95, respectively. The mean squared error of the predicted probability of surgical candidacy (Brier scores) was 0.018 in pediatrics and 0.006 in adults., Conclusions: Site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

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., (© 2020 The International Biometric Society.)

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., Competing Interests: The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2021 Jesse Pratt et al.)

A frequent problem in longitudinal studies is that data may be assessed at subject-selected, irregularly spaced time-points, resulting in highly unbalanced outcome data, inducing bias, especially if availability of data is directly related to outcome. Our aim was to develop a multivariate joint model in a mixed outcomes framework to minimize irregular sampling bias. We demonstrate using blood glucose monitoring throughout pregnancy and risk of preterm birth among women with type 1 diabetes mellitus. Blood glucose measurements were unequally spaced and intensity of sampling varied between and within individuals over time. Multivariate linear mixed effects submodel for the longitudinal outcome (blood glucose), Poisson model for the intensity of glucose sampling, and logistic regression model for binary process (preterm birth) were specified. Association between models is captured through shared random effects. Markov chain Monte Carlo methods were used to fit the model. The multivariate joint model provided better prediction, compared with a joint model with a multivariate linear mixed effects submodel (ignoring intensity of glucose sampling) and a two-stage model. Most association parameters were significant in the preterm birth outcome model, signifying improvement of predictive ability of the binary endpoint by sharing random effects between glucose monitoring and preterm birth. A simulation study is presented to illustrate the effectiveness of the multivariate joint modeling approach., (© 2021 John Wiley & Sons, Ltd.)

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., Competing Interests: The authors have no competing interests to declare., (© 2021 The Authors. Published by Elsevier B.V.)

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the 'forecast' package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

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., (©Christopher Wolfe, Teresa Pestian, Emrah Gecili, Weiji Su, Ruth H Keogh, John P Pestian, Michael Seid, Peter J Diggle, Assem Ziady, John Paul Clancy, Daniel H Grossoehme, Rhonda D Szczesniak, Cole Brokamp. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.12.2020.)

Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes., Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions., Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions., Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual's glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool., Competing Interests: The authors have no conflict of interest to declare., (© The Association for Clinical and Translational Science 2020.)

Background: Characterizing maternal glucose sampling over the course of the entire pregnancy is an important step toward improvement in prediction of adverse birth outcome, such as preterm birth, for women with type 1 diabetes mellitus (T1DM)., Objectives: To characterize the relationship between the gestational glycemic profile and risk of preterm birth using a joint modeling approach., Methods: A joint model was developed to simultaneously characterize the relationship between a longitudinal outcome (daily blood glucose sampling) and an event process (preterm birth). A linear mixed effects model using natural cubic splines was fitted to predict the longitudinal submodel. Covariates included mother's age at last menstrual period, age at diabetes onset, body mass index, hypertension, retinopathy, and nephropathy. Various association structures (value, value plus slope, and area under the curve) were examined before selecting the final joint model. We compared the joint modeling approach to the time-dependent Cox model (TDCM)., Results: A total of 16,480 glucose readings over gestation (range: 50-260 days) with 32 women (28%) having preterm birth was included in the study. Mother's age at last menstrual period and age at diabetes onset were statistically significant (beta = 1.29, 95% CI 1.10, 1.72; beta = 0.84, 95% CI 0.62, 0.98) for the longitudinal submodel, reflecting that older women tended to have higher mean blood glucose and those with later diabetes onset tended to have a lower mean blood glucose level. The presence of nephropathy was statistically significant in the event submodel (beta = 2.29, 95% CI 1.05, 4.48). Cumulative association parameterization provided the best joint model fit. The joint model provided better fit compared to the time-dependent Cox model (DIC (JM) = 19,895; DIC (TDCM) = 19,932)., Conclusion: The joint model approach was able to simultaneously characterize the glycemic profile and assess the risk of preterm birth and provided additional insights and a better model fit compared to the time-dependent Cox model., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Resmi Gupta et al.)

Background: Beginning at a young age, children with cystic fibrosis (CF) embark on demanding care regimens that pose challenges to parents. We examined the extent to which clinical, demographic and psychosocial features inform patterns of adherence to pulmonary therapies and how these patterns can be used to develop clinical personas, defined as aspects of adherence barriers that are presented by parents and/or perceived by clinicians, in order to enhance personalized CF care delivery., Methods: We undertook an explanatory sequential mixed-methods study consisting of i) multivariate clustering to create clusters corresponding to parental adherence patterns (quantitative phase); ii) parental participant interviews to create clinical personas interpreted from clustering (qualitative phase). Clinical, demographic and psychosocial features were used in supervised clustering against clinical endpoints, which included adherence to airway clearance and aerosolized medications and self-efficacy score, which was used as a feature for modeling adherence. Clinical implications were developed for each persona by combing quantitative and qualitative data (integration phase)., Results: The quantitative phase showed that the 87 parent participants were segmented into three distinct patterns of adherence based on use of aerosolized medication and practice of airway clearance. Patterns were primarily influenced by self-efficacy, distance to CF care center and child BMI percentile. The two key patterns that emerged for the self-efficacy model were most heavily influenced by distance to CF care center and child BMI percentile. Eight clinical personas were developed in the qualitative phase from parent and clinician participant feedback of latent components from these models. Findings from the integration phase include recommendations to overcome specific challenges with maintaining treatment regimens and increasing support from social networks., Conclusions: Adherence patterns from multivariate models and resulting parent personas with their corresponding clinical implications have utility as clinical decision support tools and capabilities for tailoring intervention study designs that promote adherence.

Background: Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbations (PEs) in children and adolescents with cystic fibrosis (CF). Univariate joint modeling facilitates dynamic risk prediction of PE onset and accounts for measurement error of the lung function marker. However, CF is a multi-system disease and the extent to which simultaneously modeling growth and nutrition markers improves PE predictive accuracy is unknown. Furthermore, it is unclear which routinely collected clinical indicators of growth and nutrition in early life predict PE onset in CF., Methods: Using a longitudinal cohort of 17,100 patients aged 6-20 years (US Cystic Fibrosis Foundation Patient Registry; 2003-2015), we fit a univariate joint model of lung-function decline and PE onset and contrasted its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels. Outcomes were longitudinal lung function (forced expiratory volume in 1 s of % predicted), percentiles of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in submodels. We implemented a univariate joint model of lung function and time-to-PE and four multivariate joint models including growth outcomes., Results: All five joint models showed that declining lung function corresponded to slightly increased risk of PE onset (hazard ratio from univariate joint model: 0.97, P < 0.0001), and all had reasonable predictive accuracy (cross-validated area under the receiver-operator characteristic curve > 0.70). None of the growth markers alongside lung function as outcomes in multivariate joint modeling appeared to have an association with hazard of PE. Jointly modeling only lung function and PE onset yielded the most accurate (area under the receiver-operator characteristic curve = 0.75) and precise (narrowest interquartile range) predictions. Dynamic predictions were accurate across forecast horizons (0.5, 1 and 2 years) and precision improved with age., Conclusions: Including growth markers via multivariate joint models did not yield gains in prediction performance, compared to a univariate joint model with lung function. Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient's clinical course.

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., (© 2019 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.)

Objective: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores., Methods: The application was trained on notes from (1) patients with a diagnosis of epilepsy and a history of resective epilepsy surgery and (2) patients who were seizure-free without surgery. The testing set included all patients with unknown surgical candidacy status and an upcoming neurology visit. Training and testing sets were updated weekly for 1 year. One- to three-word phrases contained in patients' notes were used as features. Patients prospectively identified by the application as candidates for surgery were manually reviewed by two epileptologists. Performance metrics were defined by comparing NLP-derived surgical candidacy scores with surgical candidacy status from expert chart review., Results: The training set was updated weekly and included notes from a mean of 519 ± 67 patients. The area under the receiver operating characteristic curve (AUC) from 10-fold cross-validation was 0.90 ± 0.04 (range = 0.83-0.96) and improved by 0.002 per week (P < .001) as new patients were added to the training set. Of the 6395 patients who visited the neurology clinic, 4211 (67%) were evaluated by the model. The prospective AUC on this test set was 0.79 (95% confidence interval [CI] = 0.62-0.96). Using the optimal surgical candidacy score threshold, sensitivity was 0.80 (95% CI = 0.29-0.99), specificity was 0.77 (95% CI = 0.64-0.88), positive predictive value was 0.25 (95% CI = 0.07-0.52), and negative predictive value was 0.98 (95% CI = 0.87-1.00). The number needed to screen was 5.6., Significance: An electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

Obesity is identified as a major global health problem. Along with measuring body mass index (BMI), the most common metric for defining weight status, health related quality of life (HRQol) has been accepted as a routine method to evaluate how body weight may be impacted by psychosocial factors. The objective of the current study is to characterize the joint association of change in longitudinal BMI and HRQol following metabolic and bariatric surgery and to examine the correlation between these two outcomes measured concurrently over time. We identified the optimal modeling strategy by comparing four models, all of which involved the covariance structures appropriate for correlated outcomes, BMI and HRQol in a repeated measures analysis. The bivariate random effects models performed better than the univariate random effects models. Moreover, bivariate models with composite covariate structures had better model fit compared to the bivariate random slope models. The bivariate models with composite covariate structures reflected that changes in HRQol (and BMI) were most significant during the first 6 months, a clinically useful window to monitor changes in post-operative HRQol and BMI, and if there might need to be additional interventions or at least, closer monitoring.

In longitudinal studies in which a medical device is used to monitor outcome repeatedly and frequently on the same patients over a prespecified duration of time, two clustering goals can arise. One goal is to assess the degree of heterogeneity among patient profiles. A second yet equally important goal unique to such studies is to determine frequency and duration of monitoring sufficient to identify longitudinal changes. Considering these goals jointly would identify clusters of patients who share similar patterns over time and characterize temporal stability within each cluster. We use a biclustering approach, allowing simultaneous clustering of observations at both patient and time levels and using a nonparametric hierarchical Bayesian model. Because clustering units at the time level (i.e., time points) are ordered and hence unexchangeable, we utilize a multivariate Dirichlet process mixture model by specifying a Dirichlet process prior at the patient level whose base measure employs change points at the time level to achieve the desired joint clustering. We consider structured covariance between consecutive time points and assess model performance through simulation studies. We apply the model to data on 24-hr ambulatory blood pressure monitoring and examine the relationship between diastolic blood pressure and pediatric obstructive sleep apnoea., Competing Interests: CONFLICT OF INTEREST The authors declare no potential conflict of interests.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients). The model was tested on 8340 notes from 3776 patients with epilepsy whose surgical candidacy status was unknown (2029 male, 1747 female, median age = 9 years; age range = 0-60 years). Multiple linear regression using demographic variables as covariates was used to test for correlations between patient race and surgical candidacy scores. After accounting for other demographic and socioeconomic variables, patient race, gender, and primary language did not influence surgical candidacy scores (P > .35 for all). Higher scores were given to patients >18 years old who traveled farther to receive care, and those who had a higher family income and public insurance (P < .001, .001, .001, and .01, respectively). Demographic effects on surgical candidacy scores appeared to reflect patterns in patient referrals., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

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.

Background: Expansion of CFTR modulators to patients with rare/undescribed mutations will be facilitated by patient-derived models quantifying CFTR function and restoration. We aimed to generate a personalized model system of CFTR function and modulation using non-surgically obtained nasal epithelial cells (NECs)., Methods: NECs obtained by curettage from healthy volunteers and CF patients were expanded and grown in 3-dimensional culture as spheroids, characterized, and stimulated with cAMP-inducing agents to activate CFTR. Spheroid swelling was quantified as a proxy for CFTR function., Results: NEC spheroids recapitulated characteristics of pseudostratified respiratory epithelia. When stimulated with forskolin/IBMX, spheroids swelled in the presence of functional CFTR, and shrank in its absence. Spheroid swelling quantified mutant CFTR restoration in F508del homozygous cells using clinically available CFTR modulators., Conclusions: NEC spheroids hold promise for understanding rare CFTR mutations and personalized modulator testing to drive evaluation for CF patients with common, rare or undescribed mutations. Portions of this data have previously been presented in abstract form at the 2016 meetings of the American Thoracic Society and the 2016 North American Cystic Fibrosis Conference., (Copyright © 2017 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.)

A two-level Gaussian process (GP) joint model is proposed to improve personalized prediction of medical monitoring data. The proposed model is applied to jointly analyze multiple longitudinal biomedical outcomes, including continuous measurements and binary outcomes, to achieve better prediction in disease progression. At the population level of the hierarchy, two independent GPs are used to capture the nonlinear trends in both the continuous biomedical marker and the binary outcome, respectively; at the individual level, a third GP, which is shared by the longitudinal measurement model and the longitudinal binary model, induces the correlation between these two model components and strengthens information borrowing across individuals. The proposed model is particularly advantageous in personalized prediction. It is applied to the motivating clinical data on cystic fibrosis disease progression, for which lung function measurements and onset of acute respiratory events are monitored jointly throughout each patient's clinical course. The results from both the simulation studies and the cystic fibrosis data application suggest that the inclusion of the shared individual-level GPs under the joint model framework leads to important improvements in personalized disease progression prediction.

Background: 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. 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., Methods: Using longitudinal lung function (FEV 1 ) 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 FEV 1 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., Results: The semiparametric joint model had the best fit of all models examined based on deviance information criterion. Higher starting FEV 1 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 FEV 1 trajectory, patients with more positive rates of change were less likely to experience a pulmonary exacerbation (HR per one standard deviation increase in FEV 1 rate of change = 0.566, 95% CI 0.516-0.619), and having higher absolute FEV 1 also corresponded to lower risk of having a pulmonary exacerbation (HR per one standard deviation increase in FEV 1  = 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 FEV 1 decline, as well as significant effects of gender, socioeconomic status and birth cohort on pulmonary exacerbation risk., Conclusions: Through 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.