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Lung function decline and variable airway inflammatory pattern: Longitudinal analysis of severe asthma  Christopher Newby, PhD, Joshua Agbetile, MRCP,

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Presentation on theme: "Lung function decline and variable airway inflammatory pattern: Longitudinal analysis of severe asthma  Christopher Newby, PhD, Joshua Agbetile, MRCP,"— Presentation transcript:

1 Lung function decline and variable airway inflammatory pattern: Longitudinal analysis of severe asthma  Christopher Newby, PhD, Joshua Agbetile, MRCP, Beverley Hargadon, RGN, Will Monteiro, MSc, Ruth Green, FRCP, MD, Ian Pavord, FRCP, MD, Christopher Brightling, FRCP, PhD, Salman Siddiqui, MRCP, PhD  Journal of Allergy and Clinical Immunology  Volume 134, Issue 2, Pages e5 (August 2014) DOI: /j.jaci Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

2 Fig 1 The lung function decline cohort was selected from our Glenfield asthma registry by using the following filters: (1) severe asthma, (2) 5 or more years of longitudinal follow-up, and (3) 3 or more serial visits with paired induced sputum and postbronchodilator spirometry. ATS, American Thoracic Society. Journal of Allergy and Clinical Immunology  , e5DOI: ( /j.jaci ) Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

3 Fig 2 Postbronchodilator FEV1 (y-axis) versus time (x-axis) in the final lung function decline model (model 30, see Table E1). The solid black line represents the overall lung function decline trajectory (decline value, −25.7 mL/y). The gray lines indicate patient-specific trajectories and were significantly influenced by log10 sputum eosinophils, which were modeled as both fixed and random effects. Journal of Allergy and Clinical Immunology  , e5DOI: ( /j.jaci ) Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

4 Fig 3 Results of clustering decrease in postbronchodilator FEV1 according to the mean log10 percentage sputum eosinophil counts and variation in eosinophilic inflammation (SD of the mean log10 percentage sputum eosinophil count). Three clusters were identified. Cluster differences are described through typical individual patient plots of log10 sputum eosinophil percentage and time (top), sputum smears (middle), and FEV1 decrease over time (bottom). Journal of Allergy and Clinical Immunology  , e5DOI: ( /j.jaci ) Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

5 Fig E1 Linear mixed models for FEV1 decrease. A, Standard expression for a simple linear regression. The FEV1 for a particular patient (i) at the time point (j) is equal to the intercept of the population (β0) plus the gradient of the population (β1) times time. B, The linear mixed model extends the simple regression, such that individual patients might have their own lung function decline trajectories. In this figure there are 2 patients (blue and red) who have distinct lung function decline trajectories. In this case the lung function decline for the patient in blue is accounted for by the intercept of the blue patient (b0i), the difference in the slope of the blue patient from the population slope term (b1i), and an error term (e). The population terms (β0 and β1) are known as fixed effects, and the individual slope and intercept terms (b1i and b0i) are known as random effects. Random effects are usually assigned to time-varying variables of interest. Journal of Allergy and Clinical Immunology  , e5DOI: ( /j.jaci ) Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

6 Fig E2 Step-by-step algorithms for 2-way cluster analysis.
Journal of Allergy and Clinical Immunology  , e5DOI: ( /j.jaci ) Copyright © 2014 American Academy of Allergy, Asthma & Immunology Terms and Conditions

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