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

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. P...

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Autores principales: Goossens, Lucas MA, Baker, Christine L, Monz, Brigitta U, Zou, Kelly H, Mölken, Maureen PMH Rutten-van
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257952/
https://www.ncbi.nlm.nih.gov/pubmed/22259243
http://dx.doi.org/10.2147/COPD.S26214
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author Goossens, Lucas MA
Baker, Christine L
Monz, Brigitta U
Zou, Kelly H
Mölken, Maureen PMH Rutten-van
author_facet Goossens, Lucas MA
Baker, Christine L
Monz, Brigitta U
Zou, Kelly H
Mölken, Maureen PMH Rutten-van
author_sort Goossens, Lucas MA
collection PubMed
description 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.
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spelling pubmed-32579522012-01-18 Adjusting for COPD severity in database research: developing and validating an algorithm Goossens, Lucas MA Baker, Christine L Monz, Brigitta U Zou, Kelly H Mölken, Maureen PMH Rutten-van Int J Chron Obstruct Pulmon Dis Original Research 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. Dove Medical Press 2011 2011-12-06 /pmc/articles/PMC3257952/ /pubmed/22259243 http://dx.doi.org/10.2147/COPD.S26214 Text en © 2011 Goossens et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
spellingShingle Original Research
Goossens, Lucas MA
Baker, Christine L
Monz, Brigitta U
Zou, Kelly H
Mölken, Maureen PMH Rutten-van
Adjusting for COPD severity in database research: developing and validating an algorithm
title Adjusting for COPD severity in database research: developing and validating an algorithm
title_full Adjusting for COPD severity in database research: developing and validating an algorithm
title_fullStr Adjusting for COPD severity in database research: developing and validating an algorithm
title_full_unstemmed Adjusting for COPD severity in database research: developing and validating an algorithm
title_short Adjusting for COPD severity in database research: developing and validating an algorithm
title_sort adjusting for copd severity in database research: developing and validating an algorithm
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257952/
https://www.ncbi.nlm.nih.gov/pubmed/22259243
http://dx.doi.org/10.2147/COPD.S26214
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