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

Autor: Maureen P.M.H. Rutten-van Mölken, Kelly H. Zou, Christine L. Baker, Lucas M A Goossens, Brigitta U. Monz
Přispěvatelé: Health Economics (HE)
Jazyk: angličtina
Rok vydání: 2011
Předmět:
Male
Databases
Factual
Osteoporosis
Psychological intervention
Severity of Illness Index
Body Mass Index
Pulmonary Disease
Chronic Obstructive
healthcare resource use
Data Mining
Multicenter Studies as Topic
Lung
Randomized Controlled Trials as Topic
Original Research
COPD
partial proportional odds logit
Smoking
Age Factors
General Medicine
Middle Aged
Obstructive lung disease
Respiratory Function Tests
Europe
Treatment Outcome
Health Resources
Female
Algorithm
Algorithms
Concordance
Logit
macromolecular substances
International Journal of Chronic Obstructive Pulmonary Disease
Sex Factors
Predictive Value of Tests
medicine
Humans
GOLD
Aged
Models
Statistical
business.industry
Australia
Univariate
Reproducibility of Results
medicine.disease
United States
respiratory tract diseases
Logistic Models
Multivariate Analysis
business
Kappa
New Zealand
Zdroj: International Journal of Chronic Obstructive Pulmonary Disease
International Journal of COPD, 2011(6), 669-678. Dove Medical Press Ltd.
ISSN: 1176-9106
Popis: Lucas MA Goossens1, Christine L Baker2, Brigitta U Monz3, Kelly H Zou2, Maureen PMH Rutten-van Mölken11Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands; 2Pfizer Inc, New York City, NY, USA; 3Boehringer Ingelheim International GmbH, Ingelheim am Rhein, GermanyPurpose: 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
Databáze: OpenAIRE
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