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Adjusting for COPD severity in database research: developing and validating an algorithm

Authors Goossens L, Baker CL, Monz, Zou KH, Rutten-van Molken MP

Published 6 December 2011 Volume 2011:6 Pages 669—678


Review by Single anonymous peer review

Peer reviewer comments 2

Lucas MA Goossens1, Christine L Baker2, Brigitta U Monz3, Kelly H Zou2, Maureen PMH Rutten-van Mölken1
1Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands; 2Pfizer Inc, New York City, NY, USA; 3Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany

Purpose: When comparing chronic obstructive lung disease (COPD) interventions in database research, it is important to adjust for severity. Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines grade severity according to lung function. Most databases lack data on lung function. Previous database research has approximated COPD severity using demographics and healthcare utilization. This study aims to derive an algorithm for COPD severity using baseline data from a large respiratory trial (UPLIFT).
Methods: Partial proportional odds logit models were developed for probabilities of being in GOLD stages II, III and IV. Concordance between predicted and observed stage was assessed using kappa-statistics. Models were estimated in a random selection of 2/3 of patients and validated in the remainder. The analysis was repeated in a subsample with a balanced distribution across severity stages. Univariate associations of COPD severity with the covariates were tested as well.
Results: More severe COPD was associated with being male and younger, having quit smoking, lower BMI, osteoporosis, hospitalizations, using certain medications, and oxygen. After adjusting for these variables, co-morbidities, previous healthcare resource use (eg, emergency room, hospitalizations) and inhaled corticosteroids, xanthines, or mucolytics were no longer independently associated with COPD severity, although they were in univariate tests. The concordance was poor (kappa = 0.151) and only slightly better in the balanced sample (kappa = 0.215).
Conclusion: COPD severity cannot be reliably predicted from demographics and healthcare use. This limitation should be considered when interpreting findings from database studies, and additional research should explore other methods to account for COPD severity.

Keywords: GOLD, healthcare resource use, partial proportional odds logit

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