title: Adjusting for COPD severity in database research: developing and validating an algorithm.
abstract: OBJECTIVE
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands. goossens@bmg.eur.nl
authors.forename: Lucas M A
authors.initials: LM
authors.lastname: Goossens
authors.forename: Christine L
authors.initials: CL
authors.lastname: Baker
authors.forename: Brigitta U
authors.initials: BU
authors.lastname: Monz
authors.forename: Kelly H
authors.initials: KH
authors.lastname: Zou
authors.forename: Maureen P M H
authors.initials: MP
authors.lastname: Rutten-van Mölken
mesh_terms: D000367:Age Factors; D000368:Aged; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D015992:Body Mass Index; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005060:Europe / Q000453:epidemiology; D005260:Female; D006295:Health Resources / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008168:Lung / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D015999:Multivariate Analysis; D009520:New Zealand / Q000453:epidemiology; D010024:Osteoporosis / Q000453:epidemiology; D011237:Predictive Value of Tests; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012129:Respiratory Function Tests; D012720:Severity of Illness Index; D012737:Sex Factors; D012907:Smoking / Q000453:epidemiology; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology