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

10.21203/rs.2.17740/v1 ◽

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.

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

10.21203/rs.2.17740/v4 ◽

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.

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

10.21203/rs.2.17740/v2 ◽

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.

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

BMC Pulmonary Medicine ◽

10.1186/s12890-020-1177-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

E. R. Andrinopoulou ◽

J. P. Clancy ◽

R. D. Szczesniak

Keyword(s):

Cystic Fibrosis ◽

Lung Function ◽

Joint Modeling ◽

Pulmonary Exacerbation ◽

Lung Function Decline ◽

Cystic Fibrosis Pulmonary Exacerbation

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

Statistical Methods in Medical Research ◽

10.1177/0962280220924680 ◽

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.

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Flexible semiparametric joint modeling: an application to estimate individual lung function decline and risk of pulmonary exacerbations in cystic fibrosis

Emerging Themes in Epidemiology ◽

10.1186/s12982-017-0067-1 ◽

2017 ◽

Vol 14 (1) ◽

Cited By ~ 6

Author(s):

Dan Li ◽

Ruth Keogh ◽

John P. Clancy ◽

Rhonda D. Szczesniak

Keyword(s):

Cystic Fibrosis ◽

Lung Function ◽

Joint Modeling ◽

Lung Function Decline ◽

Pulmonary Exacerbations

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Risk factor identification in cystic fibrosis by flexible hierarchical joint models

Statistical Methods in Medical Research ◽

10.1177/0962280220950369 ◽

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.

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