Cargando…

Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources

BACKGROUND AND OBJECTIVES: Exacerbations are important outcomes in COPD both from a clinical and an economic perspective. Most studies investigating predictors of exacerbations were performed in COPD patients participating in pharmacological clinical trials who usually have moderate to severe airflo...

Descripción completa

Detalles Bibliográficos
Autores principales: Hoogendoorn, Martine, Feenstra, Talitha L, Boland, Melinde, Briggs, Andrew H, Borg, Sixten, Jansson, Sven-Arne, Risebrough, Nancy A, Slejko, Julia F, Rutten-van Mölken, Maureen PMH
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677310/
https://www.ncbi.nlm.nih.gov/pubmed/29138546
http://dx.doi.org/10.2147/COPD.S142378
_version_ 1783277215396397056
author Hoogendoorn, Martine
Feenstra, Talitha L
Boland, Melinde
Briggs, Andrew H
Borg, Sixten
Jansson, Sven-Arne
Risebrough, Nancy A
Slejko, Julia F
Rutten-van Mölken, Maureen PMH
author_facet Hoogendoorn, Martine
Feenstra, Talitha L
Boland, Melinde
Briggs, Andrew H
Borg, Sixten
Jansson, Sven-Arne
Risebrough, Nancy A
Slejko, Julia F
Rutten-van Mölken, Maureen PMH
author_sort Hoogendoorn, Martine
collection PubMed
description BACKGROUND AND OBJECTIVES: Exacerbations are important outcomes in COPD both from a clinical and an economic perspective. Most studies investigating predictors of exacerbations were performed in COPD patients participating in pharmacological clinical trials who usually have moderate to severe airflow obstruction. This study was aimed to investigate whether predictors of COPD exacerbations depend on the COPD population studied. METHODS: A network of COPD health economic modelers used data from five COPD data sources – two population-based studies (COPDGene(®) and The Obstructive Lung Disease in Norrbotten), one primary care study (RECODE), and two studies in secondary care (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoint and UPLIFT) – to estimate and validate several prediction models for total and severe exacerbations (= hospitalization). The models differed in terms of predictors (depending on availability) and type of model. RESULTS: FEV(1)% predicted and previous exacerbations were significant predictors of total exacerbations in all five data sources. Disease-specific quality of life and gender were predictors in four out of four and three out of five data sources, respectively. Age was significant only in the two studies including secondary care patients. Other significant predictors of total exacerbations available in one database were: presence of cough and wheeze, pack-years, 6-min walking distance, inhaled corticosteroid use, and oxygen saturation. Predictors of severe exacerbations were in general the same as for total exacerbations, but in addition low body mass index, cardiovascular disease, and emphysema were significant predictors of hospitalization for an exacerbation in secondary care patients. CONCLUSIONS: FEV(1)% predicted, previous exacerbations, and disease-specific quality of life were predictors of exacerbations in patients regardless of their COPD severity, while age, low body mass index, cardiovascular disease, and emphysema seem to be predictors in secondary care patients only.
format Online
Article
Text
id pubmed-5677310
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Dove Medical Press
record_format MEDLINE/PubMed
spelling pubmed-56773102017-11-14 Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources Hoogendoorn, Martine Feenstra, Talitha L Boland, Melinde Briggs, Andrew H Borg, Sixten Jansson, Sven-Arne Risebrough, Nancy A Slejko, Julia F Rutten-van Mölken, Maureen PMH Int J Chron Obstruct Pulmon Dis Original Research BACKGROUND AND OBJECTIVES: Exacerbations are important outcomes in COPD both from a clinical and an economic perspective. Most studies investigating predictors of exacerbations were performed in COPD patients participating in pharmacological clinical trials who usually have moderate to severe airflow obstruction. This study was aimed to investigate whether predictors of COPD exacerbations depend on the COPD population studied. METHODS: A network of COPD health economic modelers used data from five COPD data sources – two population-based studies (COPDGene(®) and The Obstructive Lung Disease in Norrbotten), one primary care study (RECODE), and two studies in secondary care (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoint and UPLIFT) – to estimate and validate several prediction models for total and severe exacerbations (= hospitalization). The models differed in terms of predictors (depending on availability) and type of model. RESULTS: FEV(1)% predicted and previous exacerbations were significant predictors of total exacerbations in all five data sources. Disease-specific quality of life and gender were predictors in four out of four and three out of five data sources, respectively. Age was significant only in the two studies including secondary care patients. Other significant predictors of total exacerbations available in one database were: presence of cough and wheeze, pack-years, 6-min walking distance, inhaled corticosteroid use, and oxygen saturation. Predictors of severe exacerbations were in general the same as for total exacerbations, but in addition low body mass index, cardiovascular disease, and emphysema were significant predictors of hospitalization for an exacerbation in secondary care patients. CONCLUSIONS: FEV(1)% predicted, previous exacerbations, and disease-specific quality of life were predictors of exacerbations in patients regardless of their COPD severity, while age, low body mass index, cardiovascular disease, and emphysema seem to be predictors in secondary care patients only. Dove Medical Press 2017-11-01 /pmc/articles/PMC5677310/ /pubmed/29138546 http://dx.doi.org/10.2147/COPD.S142378 Text en © 2017 Hoogendoorn et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Hoogendoorn, Martine
Feenstra, Talitha L
Boland, Melinde
Briggs, Andrew H
Borg, Sixten
Jansson, Sven-Arne
Risebrough, Nancy A
Slejko, Julia F
Rutten-van Mölken, Maureen PMH
Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title_full Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title_fullStr Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title_full_unstemmed Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title_short Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources
title_sort prediction models for exacerbations in different copd patient populations: comparing results of five large data sources
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677310/
https://www.ncbi.nlm.nih.gov/pubmed/29138546
http://dx.doi.org/10.2147/COPD.S142378
work_keys_str_mv AT hoogendoornmartine predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT feenstratalithal predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT bolandmelinde predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT briggsandrewh predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT borgsixten predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT janssonsvenarne predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT risebroughnancya predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT slejkojuliaf predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources
AT ruttenvanmolkenmaureenpmh predictionmodelsforexacerbationsindifferentcopdpatientpopulationscomparingresultsoffivelargedatasources