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Predictors of chronic obstructive pulmonary disease progression








Predictors of chronic obstructive pulmonary disease progression


Akihiro Hisaka


Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba university, Chiba, Japan


None


None


13 September 2017


Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung syndrome that affects millions of people, and it is estimated to become the third leading cause of death worldwide in 2030. Although it is well known that COPD is characterized by a progressive and life-long airflow limitation, the rate of decline in lung function is extremely variable across patients.
The lung function of patients with COPD is confirmed using a pulmonary function test called “spirometry”. A key value of spirometry is the forced expiratory volume in 1 s (FEV1), which is used to stage COPD severity according to the Global Initiative for Chronic Obstructive Lung Disease guidelines. People with COPD demonstrate a decline in FEV1 over time, and the FEV1 can be crucial in choosing the best treatment option and predicting prognosis.

Previously, a number of clinical studies have been conducted to analyze FEV1 change and its influencing factors in patients with COPD. However, few studies have focused on lifelong FEV1 change because it is practically difficult to perform a cohort study over several decades. Moreover, although some investigations of an FEV1 prediction model have been performed, no model with the ability to predict lifelong changes in FEV1 in patients with COPD has been developed. Therefore, the aim of this study is to develop a lifelong FEV1 model and identify its influencing factors. Because it is practically difficult to perform a cohort study over several decades, we are planning to assume decades-long FEV1 longitudinal change by using a model-based meta-analysis with Statistical Restoration of Fragmented Time-course (SReFT). SReFT is a new method we previously developed to restore long-term time courses from numerous short fragments by using an extended nonlinear mixed-effects estimation method.

This study would allow the prediction of lifelong FEV1 change in COPD patients based on their background. It would provide useful information in predicting prognosis and choosing the best treatment option for each patient with COPD. In addition, this study is expected to propose a novel and efficient approach for understanding disease progression in chronic diseases. SReFT can be applied not only to COPD but to almost all chronic diseases. With SReFT, we will be able to assume long-term disease progression without conducting long-term clinical studies.



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Medicine: fluticasone furoate/vilanterol, Condition: Pulmonary Disease, Chronic Obstructive, Phase: 3, Clinical Study ID: HZC113782, Sponsor: GSK" }]

Statistical Analysis Plan