Research Article: A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT® Daily Diary Data from COPD Patients

Date Published: April 26, 2019

Publisher: Springer International Publishing

Author(s): Eva Germovsek, Claire Ambery, Shuying Yang, Misba Beerahee, Mats O. Karlsson, Elodie L. Plan.

http://doi.org/10.1208/s12248-019-0319-9

Abstract

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT®) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67 years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT® item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.

Partial Text

Chronic obstructive pulmonary disease (COPD) is an inflammatory disease of the lung, characterised by airflow obstruction that progresses with time. The most important risk factor associated with COPD is considered smoking, but risk factors also include other exposures (e.g. air pollution, occupational dusts and chemicals) and host factors, such as α1-antitrypsin deficiency (1). COPD is associated with emphysema and mucus hypersecretion, and its progression is punctuated with acute periods of a temporary increase in symptoms, also called exacerbations (2,3).Historically, exacerbations are defined by a clinic visit or hospitalisation with medical treatment (clinically confirmed); however, recently, questionnaires have been validated as useful for symptom-defined exacerbations (4–6). Exacerbations contribute to an accelerated decline of pulmonary function, higher risk of cardiovascular events (7) and worse quality of life (8) and are a major cause of COPD-related hospital admissions, morbidity and mortality; therefore, also increasing healthcare costs. Approximately 174 million people had COPD in 2015 (9), and around three million die from it every year (10). The disease burden of COPD is third worldwide (11,12), but may even increase in the future, due to an ageing society.

A combination of an item response theory model and 14 item-specific longitudinal Markov models was successfully developed for the first time to our knowledge. This integrated modelling approach proved to be able to describe frequently collected and therefore correlated composite score data, as was exemplified here using daily EXACT® patient reported outcome data from patients with COPD receiving standard of care only.

 

Source:

http://doi.org/10.1208/s12248-019-0319-9

 

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