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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.
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Teorema de Bayes , Exposição Ambiental , Humanos , Exposição Ambiental/estatística & dados numéricos , Feminino , Masculino , Estudos Retrospectivos , Adolescente , Criança , Adulto Jovem , Progressão da Doença , Poluição do Ar/estatística & dados numéricos , Estudos Longitudinais , Fibrose Cística , Pneumopatias/epidemiologia , Poluentes Atmosféricos/análiseRESUMO
RATIONALE: Declines in percent predicted Forced Expiratory Volume in 1 Second (ppFEV1) are an important marker of clinical progression of Cystic Fibrosis (CF). OBJECTIVES: We examined ppFEV1 variability on a combined outcome of lung transplant or death. METHODS: We estimated the association between ppFEV1 variability and the combined outcome of lung transplant or death. We included children ages 8 years and above with CF and two prior years of ppFEV1 data before baseline between 2005 and 2021. We defined ppFEV1 increased variability as any relative increase or decrease of at least 10% in ppFEV1 from a two-year averaged baseline. A marginal structural Cox proportional hazards model was used. We examined a cumulative measure of ppFEV1 variability, defined as the cumulative proportion of visits with ppFEV1 variability at each visit. Kaplan-Meier survival curves were generated based upon quartiles of the cumulative distribution of ppFEV1 variability. MEASUREMENTS AND MAIN RESULTS: We included 9,706 CF patients in our cohort. Median age at cohort entry was 8.3 (IQR 8.2 - 8.4) years, 50% of patients were female, 94% white, and median baseline ppFEV1 was 94.4 (IQR 81.6 - 106.1). The unadjusted HR for increased ppFEV1 variability on lung transplant/mortality was 4.13 (95% CI 3.48 - 4.90) and the weighted HR was 1.49 (95% CI 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 ppFEV1 variability increased. CONCLUSIONS: We found a strong association between ppFEV1 variability and lung transplant or mortality in a cohort of people with CF in the US.
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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.
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Aminofenóis , Fibrose Cística , Pulmão , Quinolonas , Humanos , Fibrose Cística/tratamento farmacológico , Fibrose Cística/fisiopatologia , Fibrose Cística/diagnóstico , Fibrose Cística/genética , Aminofenóis/uso terapêutico , Feminino , Masculino , Estudos Retrospectivos , Estudos Longitudinais , Quinolonas/uso terapêutico , Adulto , Adolescente , Adulto Jovem , Volume Expiratório Forçado/fisiologia , Pulmão/efeitos dos fármacos , Pulmão/fisiopatologia , Criança , Regulador de Condutância Transmembrana em Fibrose Cística/genética , Agonistas dos Canais de Cloreto/uso terapêutico , Valor Preditivo dos Testes , Sistema de Registros , Testes de Função Respiratória/métodos , Progressão da Doença , Estudos de Coortes , Resultado do TratamentoRESUMO
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.
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Fibrose Cística , Espirometria , Humanos , Fibrose Cística/fisiopatologia , Fibrose Cística/etnologia , Masculino , Volume Expiratório Forçado , Feminino , Estudos Transversais , Adulto , Adolescente , Espirometria/métodos , Criança , Estados Unidos/epidemiologia , Adulto Jovem , Valores de Referência , Sistema de RegistrosRESUMO
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.
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Epilepsia , Adulto , Humanos , Criança , Estudos Longitudinais , Epilepsia/diagnóstico , Epilepsia/cirurgia , Estudos Prospectivos , Estudos de Coortes , Aprendizado de Máquina , Estudos RetrospectivosRESUMO
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.
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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.
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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.
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Fibrose Cística , Humanos , Teorema de Bayes , Simulação por Computador , Análise Multinível , Pulmão , Estudos LongitudinaisRESUMO
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.
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Fibrose Cística , Poluição por Fumaça de Tabaco , Adolescente , Criança , Humanos , Fibrose Cística/epidemiologia , Poluição por Fumaça de Tabaco/efeitos adversos , Teorema de Bayes , PulmãoRESUMO
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.
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Registros Eletrônicos de Saúde , Epilepsia , Humanos , Criança , Estudos Prospectivos , Aprendizado de Máquina , Epilepsia/diagnóstico , Epilepsia/cirurgia , Encaminhamento e ConsultaRESUMO
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.
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Fibrose Cística , Adolescente , Humanos , Adulto , Estudos Longitudinais , Estudos Retrospectivos , Estudos de Coortes , Pulmão , Volume Expiratório ForçadoRESUMO
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.
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Neoplasias Pulmonares , Transplante de Pulmão , Linfangioleiomiomatose , Humanos , Linfangioleiomiomatose/tratamento farmacológico , Pulmão , Progressão da Doença , Volume Expiratório ForçadoRESUMO
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.
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Fibrose Cística , Infecções por Mycobacterium não Tuberculosas , Adulto , Fibrose Cística/complicações , Humanos , Imunidade , Infecções por Mycobacterium não Tuberculosas/complicações , Infecções por Mycobacterium não Tuberculosas/microbiologia , Micobactérias não TuberculosasRESUMO
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.
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Hipertensão , Apneia Obstrutiva do Sono , Teorema de Bayes , Pressão Sanguínea/fisiologia , Monitorização Ambulatorial da Pressão Arterial , Criança , Ritmo Circadiano , HumanosRESUMO
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.
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Fibrose Cística , Antibacterianos/uso terapêutico , Fibrose Cística/complicações , Fibrose Cística/diagnóstico , Fibrose Cística/tratamento farmacológico , Humanos , Metabolômica , Estudos Prospectivos , SuorRESUMO
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.
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Genômica , Proteômica , Teorema de Bayes , Humanos , Modelos Lineares , Distribuição NormalRESUMO
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 FEV1% 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.
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Fibrose Cística , Aminofenóis/uso terapêutico , Antibacterianos/uso terapêutico , Benzodioxóis/uso terapêutico , Fibrose Cística/tratamento farmacológico , Fibrose Cística/genética , Regulador de Condutância Transmembrana em Fibrose Cística/genética , Humanos , Mutação , Estudos Observacionais como Assunto , Ensaios Clínicos Controlados Aleatórios como Assunto , Sistema de RegistrosRESUMO
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.
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Fibrose Cística , Infecções por Pseudomonas , Adolescente , Adulto , Criança , Estudos Transversais , Fibrose Cística/tratamento farmacológico , Fibrose Cística/epidemiologia , Humanos , Pulmão , Infecções por Pseudomonas/tratamento farmacológico , Infecções por Pseudomonas/epidemiologia , Pseudomonas aeruginosa , Sistema de Registros , Staphylococcus aureus , Reino Unido/epidemiologiaRESUMO
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.
RESUMO
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.â¢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.â¢Steps required for mediation analyses in the seasonality model are shown.â¢The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study.