scholarly journals Risk factor identification in cystic fibrosis by flexible hierarchical joint models

2020 ◽  
pp. 096228022095036
Author(s):  
Weiji Su ◽  
Xia Wang ◽  
Rhonda D Szczesniak
Keyword(s):  
Risk Factors ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Gaussian Process ◽  
Joint Model ◽  
Model Assessment ◽  
Link Function ◽  
Pulmonary Exacerbation ◽  
Flexible Link ◽  
Joint Models

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

scholarly journals Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

2020 ◽  
Vol 29 (11) ◽  
pp. 3294-3307
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
Kazem Nasserinejad ◽  
Rhonda Szczesniak ◽  
Dimitris Rizopoulos
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Latent Class ◽  
Joint Model ◽  
Forced Expiratory Volume ◽  
Optimal Number ◽  
Latent Classes ◽  
Joint Models ◽  
Number Of Classes ◽  
Strength Of Association

Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically “emptying” superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s ( FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2019 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract Background: Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbation (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), percentile measures of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in each submodel. 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. However, the joint-modeling approach itself may be useful for monitoring CF disease progression by providing a means of dynamic risk prediction.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2020 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract 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.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2020 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract 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.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2020 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract 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.

scholarly journals Incidence and risk factors for frequent pulmonary exacerbation in cystic fibrosis

2010 ◽  
Vol 9 ◽  
pp. S110 ◽  
Author(s):  
M.D. Parkins ◽  
M. Ibrahim ◽  
J.C. Rendall ◽  
J.S. Elborn
Keyword(s):  
Risk Factors ◽  
Cystic Fibrosis ◽  
Pulmonary Exacerbation

Risk factors leading to pulmonary exacerbation in patients with cystic fibrosis: A systematic review

2021 ◽  
Vol 71 (9) ◽  
Author(s):  
Danish Abdul Aziz ◽  
Syeda Khadija Fatima ◽  
Hasan Nawaz Tahir
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Cystic Fibrosis ◽  
Meta Analysis ◽  
Nutritional Deficiencies ◽  
Pulmonary Exacerbation ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Factors Associated ◽  
Randomised Controlled

Objective: To ascertain major risk factors associated with pulmonary exacerbation and pulmonary function decline in cystic fibrosis. Method: The systematic review was conducted at Aga Khan University, Karachi, in September 2018, and comprised electronic search of PubMed, Ovid, Science Direct and Cumulative Index of Nursing and Allied Health Literature databases of studies conducted from January 1990 to September 2018 which were categorised into 3 sets; 1990-98, 1999-2007 and 2008-18. Studies included for review focussed on articles with pulmonary exacerbation as the health outcome indicator, and had diagnosis of cystic fibrosis as the inclusion criteria, while risk factors were the exposure terms used in the search process. References in bibliographies of the included studies were also systematically searched for relevant documents. Results: Of the 60 studies obtained, 31(51.7%) were selected; 2(6.45%) from 1990-98, 7(22.58%) from 1999-2007 and 22(70.96%) from 2008-18. Overall, 17(54.83%) were cohort studies, 7(22.5%) were cross-sectional studies, 3(9.6%) were case-control studies, 3(9.6%) were randomised controlled trials and 1(3.2%) was systematic review and meta-analysis. In terms of major risk factors, genetic mutations were cited by 4(12.9%) studies, infections and inflammatory biomarkers by 15(48.4%), nutritional deficiencies by 9(29%) and geographical and socioeconomic status by 3(9.6%) studies. Conclusion: Early identification and recognition of risk factors associated with pulmonary exacerbation can have an explicit impact on its management, leading to decreased morbidity and mortality burden in cystic fibrosis cases. Key Words: Pulmonary exacerbation, Cystic fibrosis, Risk factors, Systematic review. Continuous...

scholarly journals Risk factors for respiratory Aspergillus fumigatus in German Cystic Fibrosis patients and impact on lung function

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Uta Düesberg ◽  
Julia Wosniok ◽  
Lutz Naehrlich ◽  
Patience Eschenhagen ◽  
Carsten Schwarz
Keyword(s):  
Risk Factors ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Aspergillus Fumigatus ◽  
Age Groups ◽  
Antibiotic Use ◽  
Lower Lung ◽  
Lung Infections ◽  
The Impact

Abstract Airway inflammation and chronic lung infections in cystic fibrosis (CF) patients are mostly caused by bacteria, e.g. Pseudomonas aeruginosa (PA). The role of fungi in the CF lung is still not well elucidated, but evidence for a harmful and complex role is getting stronger. The most common filamentous fungus in CF is Aspergillus fumigatus (AF). Age and continuous antibiotic treatment have been discussed as risk factors for AF colonisation but did not differentiate between transient and persistent AF colonisation. Also, the impact of co-colonisation of PA and AF on lung function is still under investigation. Data from patients with CF registered in the German Cystic Fibrosis Registry database in 2016 and 2017 were retrospectively analysed, involving descriptive and multivariate analysis to assess risk factors for transient or persistent AF colonisation. Age represented an independent risk factor for persistent AF colonisation. Prevalence was low in children less than ten years, highest in the middle age and getting lower in higher age (≥ 50 years). Continuous antibiotic lung treatment was significantly associated with AF prevalence in all age groups. CF patients with chronic PA infection had a lower lung function (FEV1%predicted), which was not influenced by an additional AF colonisation. AF colonisation without chronic PA infection, however, was significantly associated with a lower function, too. Older age up to 49 years and continuous antibiotic use were found to be the main risk factors for AF permanent colonisation. AF might be associated with decrease of lung function if not disguised by chronic PA infection.

scholarly journals Risk factors for lung function decline in a large cohort of young cystic fibrosis patients

2015 ◽  
Vol 50 (8) ◽  
pp. 763-770 ◽  
Author(s):  
Jonathan Cogen ◽  
Julia Emerson ◽  
Don B. Sanders ◽  
Clement Ren ◽  
Michael S. Schechter ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Large Cohort ◽  
Lung Function Decline
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