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

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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Pulmonary exacerbations as a risk factor for lung function decline-experiences of the National Cystic Fibrosis Center

Vojnosanitetski pregled ◽

10.2298/vsp171114005g ◽

2019 ◽

Vol 76 (11) ◽

pp. 1110-1114

Author(s):

Bojana Gojsina ◽

Milan Rodic ◽

Jelena Visekruna ◽

Goran Trajkovic ◽

Aleksandar Sovtic ◽

...

Keyword(s):

Cystic Fibrosis ◽

Lung Function ◽

Burkholderia Cepacia ◽

Negative Impact ◽

Bacterial Colonization ◽

Forced Expiratory Volume ◽

Lung Function Decline ◽

Pulmonary Exacerbations ◽

Fev1 Decline

Background/Aim. Pulmonary exacerbations have negative impact on clinical course of cystic fibrosis (CF) lung disease being associated with a steeper decline in the lung function, unfavorable prognosis and impaired quality of life. The aim of this study was to determine whether an increased number of exacerbations had influence on the lung function in the patients with CF, as well as to estimate the nutritional status, gender, presence of comorbid conditions and bacterial colonization of airways as predictive factors for pulmonary exacerbations. Methods. This retrospective cohort study included 83 pediatric and adult patients, treated from 2011? 2015 in the Mother and Child Health Institute of Serbia ?Dr Vukan Cupic?. The best result of forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) in each year of follow-up was taken into account to calculate the five-year trend values of these indicators. The number of exacerbations per year of follow-up and its impact on the FEV1 decline was evaluated. Results. Mean annual decline of FEV1 and FVC were 2.4% and 1.7% respectively. The malnourished patients had the lower initial values of FEV1 and FVC, and more frequent exacerbations in comparison with the normal weight and overweight patients. The frequency of exacerbations was significantly higher in the patients chronically colonized with Burkholderia cepacia (p = 0.023). The increased number of exacerbation was proved to be the most important factor in a prediction of FEV1 decline over time (p = 0.013). Conclusion. Pulmonary exacerbations lead to the more progressive lung function decline in the patients with CF. Malnourishment and chronic airway colonization with Burkholderia cepacia result in more frequent pulmonary exacerbations.

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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/v3 ◽

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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Effect of pulmonary exacerbations on long-term lung function decline in cystic fibrosis

European Respiratory Journal ◽

10.1183/09031936.00159111 ◽

2011 ◽

Vol 40 (1) ◽

pp. 61-66 ◽

Cited By ~ 127

Author(s):

Valerie Waters ◽

Sanja Stanojevic ◽

Eshetu G. Atenafu ◽

Annie Lu ◽

Yvonne Yau ◽

...

Keyword(s):

Cystic Fibrosis ◽

Lung Function ◽

Lung Function Decline ◽

Pulmonary Exacerbations

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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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